Resumo: Os alunos de graduação são frequentemente afetados por depressão, ansiedade e estresse. O aprendizado de máquina pode apoiar a avaliação da saúde mental. Com base na seguinte questão de pesquisa “Qual é o desempenho dos modelos de aprendizado de máquina na detecção de depressão, ansiedade e estresse entre estudantes de graduação?”, objetivou-se avaliar o desempenho desses modelos. As pesquisas foram realizadas no PubMed, Embase, PsycINFO e Web of Science. Foram pesquisados estudos que atendessem aos seguintes critérios: publicados em inglês, estudantes universitários de graduação como população alvo, empíricos, publicados em uma revista científica e que previssem resultados de ansiedade, depressão ou estresse via aprendizado de máquina. A qualidade das evidências foi analisada usando o GRADE. Em janeiro de 2024, foram encontrados 2.304 artigos, e 48 estudos atenderam aos critérios de inclusão. Foram identificados diferentes tipos de dados, incluindo dados comportamentais, fisiológicos, de uso da Internet, neurocerebrais, marcadores sanguíneos, dados mistos, demográficos e de mobilidade. Entre os 33 estudos que forneceram dados de precisão, 30 relataram valores superiores a 70%. A acurácia na detecção de estresse variou de 63% a 100%, ansiedade de 53,69% a 97,9% e depressão de 73,5% a 99,1%. Embora a maioria dos modelos apresente desempenho adequado, deve-se notar que 47 deles realizaram apenas validação interna, o que pode superestimar os dados de desempenho. Além disso, a avaliação GRADE indicou que a qualidade da evidência é muito baixa. Os resultados indicam que os algoritmos de aprendizado de máquina são promissores no campo da Saúde Pública; no entanto, é crucial examinar sua aplicabilidade prática. Estudos futuros devem investir principalmente na validação externa dos modelos de aprendizado de máquina.

Resumen: Los estudiantes de grado suelen verse afectados por la depresión, la ansiedad y el estrés. El aprendizaje automático puede respaldar la evaluación de la salud mental. Con base en la siguiente pregunta de investigación “¿Cuál es el rendimiento de los modelos de aprendizaje automático en la detección de depresión, ansiedad y estrés entre estudiantes universitarios?”, nuestro objetivo fue evaluar el rendimiento de estos modelos. Se realizaron búsquedas en PubMed, Embase, PsycINFO y Web of Science. Se buscaron estudios que cumplieran con los siguientes criterios: se hubieran publicado en inglés, tuvieran a estudiantes universitarios como población objetivo, fueran empíricos, publicados en una revista científica y que predijeran resultados de ansiedad, depresión o estrés mediante aprendizaje automático. La calidad de las evidencias se analizó mediante GRADE. En enero del 2024 se encontraron 2.304 artículos, y 48 estudios cumplieron con los criterios de inclusión. Se identificaron diferentes tipos de datos, incluidos datos conductuales, fisiológicos, de uso de internet, neurocerebrales, marcadores sanguíneos, datos mixtos, demográficos y de movilidad. Entre los 33 estudios que proporcionaron datos de precisión, 30 reportaron valores superiores al 70%. La precisión en la detección del estrés osciló entre el 63% y el 100%, la ansiedad del 53,69% al 97,9% y la depresión del 73,5% al 99,1%. Aunque la mayoría de los modelos presenta un rendimiento adecuado, cabe señalar que 47 de ellos realizaron únicamente validación interna, lo que puede sobrestimar los datos de rendimiento. Además, la evaluación GRADE indicó que la calidad de la evidencia es muy baja. Los resultados indican que los algoritmos de aprendizaje automático son prometedores en el campo de la Salud Pública; sin embargo, es crucial examinar su aplicabilidad práctica. Los estudios futuros deberían invertir principalmente en la validación externa de los modelos de aprendizaje automático.

Abstract: Undergraduate students are often impacted by depression, anxiety, and stress. In this context, machine learning may support mental health assessment. Based on the following research question: “How do machine learning models perform in the detection of depression, anxiety, and stress among undergraduate students?”, we aimed to evaluate the performance of these models. PubMed, Embase, PsycINFO, and Web of Science databases were searched, aiming at studies meeting the following criteria: publication in English; targeting undergraduate university students; empirical studies; having been published in a scientific journal; and predicting anxiety, depression, or stress outcomes via machine learning. The certainty of evidence was analyzed using the GRADE. As of January 2024, 2,304 articles were found, and 48 studies met the inclusion criteria. Different types of data were identified, including behavioral, physiological, internet usage, neurocerebral, blood markers, mixed data, as well as demographic and mobility data. Among the 33 studies that provided accuracy assessment, 30 reported values that exceeded 70%. Accuracy in detecting stress ranged from 63% to 100%, anxiety from 53.69% to 97.9%, and depression from 73.5% to 99.1%. Although most models present adequate performance, it should be noted that 47 of them only performed internal validation, which may overstate the performance data. Moreover, the GRADE checklist suggested that the quality of the evidence was very low. These findings indicate that machine learning algorithms hold promise in Public Health; however, it is crucial to scrutinize their practical applicability. Further studies should invest mainly in external validation of the machine learning models.

Resumo: Os alunos de graduação são frequentemente afetados por depressão, ansiedade e estresse. O aprendizado de máquina pode apoiar a avaliação da saúde mental. Com base na seguinte questão de pesquisa “Qual é o desempenho dos modelos de aprendizado de máquina na detecção de depressão, ansiedade e estresse entre estudantes de graduação?”, objetivou-se avaliar o desempenho desses modelos. As pesquisas foram realizadas no PubMed, Embase, PsycINFO e Web of Science. Foram pesquisados estudos que atendessem aos seguintes critérios: publicados em inglês, estudantes universitários de graduação como população alvo, empíricos, publicados em uma revista científica e que previssem resultados de ansiedade, depressão ou estresse via aprendizado de máquina. A qualidade das evidências foi analisada usando o GRADE. Em janeiro de 2024, foram encontrados 2.304 artigos, e 48 estudos atenderam aos critérios de inclusão. Foram identificados diferentes tipos de dados, incluindo dados comportamentais, fisiológicos, de uso da Internet, neurocerebrais, marcadores sanguíneos, dados mistos, demográficos e de mobilidade. Entre os 33 estudos que forneceram dados de precisão, 30 relataram valores superiores a 70%. A acurácia na detecção de estresse variou de 63% a 100%, ansiedade de 53,69% a 97,9% e depressão de 73,5% a 99,1%. Embora a maioria dos modelos apresente desempenho adequado, deve-se notar que 47 deles realizaram apenas validação interna, o que pode superestimar os dados de desempenho. Além disso, a avaliação GRADE indicou que a qualidade da evidência é muito baixa. Os resultados indicam que os algoritmos de aprendizado de máquina são promissores no campo da Saúde Pública; no entanto, é crucial examinar sua aplicabilidade prática. Estudos futuros devem investir principalmente na validação externa dos modelos de aprendizado de máquina. Resumo mental Qual graduação, , graduação?” objetivouse objetivou se PubMed Embase Science inglês alvo empíricos 2024 2304 2 304 2.30 artigos 4 inclusão comportamentais fisiológicos Internet neurocerebrais sanguíneos mistos mobilidade 3 precisão 70 70% 63 100 100% 5369 53 69 53,69 979 97 9 97,9 735 73 5 73,5 991 99 1 99,1% adequado devese deve interna disso baixa Pública entanto prática graduação? 202 230 2.3 7 6 10 536 53,6 97, 73, 99,1 20 23 2. 53, 99,

Resumen: Los estudiantes de grado suelen verse afectados por la depresión, la ansiedad y el estrés. El aprendizaje automático puede respaldar la evaluación de la salud mental. Con base en la siguiente pregunta de investigación “¿Cuál es el rendimiento de los modelos de aprendizaje automático en la detección de depresión, ansiedad y estrés entre estudiantes universitarios?”, nuestro objetivo fue evaluar el rendimiento de estos modelos. Se realizaron búsquedas en PubMed, Embase, PsycINFO y Web of Science. Se buscaron estudios que cumplieran con los siguientes criterios: se hubieran publicado en inglés, tuvieran a estudiantes universitarios como población objetivo, fueran empíricos, publicados en una revista científica y que predijeran resultados de ansiedad, depresión o estrés mediante aprendizaje automático. La calidad de las evidencias se analizó mediante GRADE. En enero del 2024 se encontraron 2.304 artículos, y 48 estudios cumplieron con los criterios de inclusión. Se identificaron diferentes tipos de datos, incluidos datos conductuales, fisiológicos, de uso de internet, neurocerebrales, marcadores sanguíneos, datos mixtos, demográficos y de movilidad. Entre los 33 estudios que proporcionaron datos de precisión, 30 reportaron valores superiores al 70%. La precisión en la detección del estrés osciló entre el 63% y el 100%, la ansiedad del 53,69% al 97,9% y la depresión del 73,5% al 99,1%. Aunque la mayoría de los modelos presenta un rendimiento adecuado, cabe señalar que 47 de ellos realizaron únicamente validación interna, lo que puede sobrestimar los datos de rendimiento. Además, la evaluación GRADE indicó que la calidad de la evidencia es muy baja. Los resultados indican que los algoritmos de aprendizaje automático son prometedores en el campo de la Salud Pública; sin embargo, es crucial examinar su aplicabilidad práctica. Los estudios futuros deberían invertir principalmente en la validación externa de los modelos de aprendizaje automático. Resumen mental ¿Cuál Cuál universitarios, , universitarios?” PubMed Embase Science inglés empíricos 202 2304 2 304 2.30 artículos 4 inclusión conductuales fisiológicos internet neurocerebrales sanguíneos mixtos movilidad 3 70 70% 63 100 100% 5369 53 69 53,69 979 97 9 97,9 735 73 5 73,5 991 99 1 99,1% adecuado interna Además baja Pública embargo práctica universitarios? 20 230 2.3 7 6 10 536 53,6 97, 73, 99,1 23 2. 53, 99,

Abstract: Undergraduate students are often impacted by depression, anxiety, and stress. In this context, machine learning may support mental health assessment. Based on the following research question: “How do machine learning models perform in the detection of depression, anxiety, and stress among undergraduate students?”, we aimed to evaluate the performance of these models. PubMed, Embase, PsycINFO, and Web of Science databases were searched, aiming at studies meeting the following criteria: publication in English; targeting undergraduate university students; empirical studies; having been published in a scientific journal; and predicting anxiety, depression, or stress outcomes via machine learning. The certainty of evidence was analyzed using the GRADE. As of January 2024, 2,304 articles were found, and 48 studies met the inclusion criteria. Different types of data were identified, including behavioral, physiological, internet usage, neurocerebral, blood markers, mixed data, as well as demographic and mobility data. Among the 33 studies that provided accuracy assessment, 30 reported values that exceeded 70%. Accuracy in detecting stress ranged from 63% to 100%, anxiety from 53.69% to 97.9%, and depression from 73.5% to 99.1%. Although most models present adequate performance, it should be noted that 47 of them only performed internal validation, which may overstate the performance data. Moreover, the GRADE checklist suggested that the quality of the evidence was very low. These findings indicate that machine learning algorithms hold promise in Public Health; however, it is crucial to scrutinize their practical applicability. Further studies should invest mainly in external validation of the machine learning models. Abstract context assessment question How students, , students?” PubMed Embase PsycINFO searched criteria English journal 2024 2304 2 304 2,30 found 4 identified behavioral physiological usage neurocerebral markers 3 70 70% 63 100 100% 5369 53 69 53.69 979 97 9 97.9% 735 73 5 73.5 991 99 1 99.1% Moreover low Health however applicability students? 202 230 2,3 7 6 10 536 53.6 97.9 73. 99.1 20 23 2, 53. 97. 99.

Resumo Objetivo Desenvolver uma diretriz clínica híbrida para atendimento pré-natal de baixo risco, mesclando consultas presenciais e remotas por telemedicina, adapta às recomendações brasileiras. Métodos Revisão sistemática da literatura nas bases de dados PubMed, Embase e Cochrane e adaptação dos protocolos de atenção ao pré-natal de baixo risco preconizados pelo Ministério da Saúde e pela Federação Brasileira das Associações de Ginecologia e Obstetrícia. Resultados Cinco artigos relevantes e três manuais foram incluídos na revisão por preencherem critérios para o desenvolvimento desta diretriz clínica. Nos estudos incluídos, identificou-se que o cronograma de consultas se distribui de forma desigual entre os trimestres gestacionais, variando entre 07 e 14 encontros. De forma geral, os autores propõem uma a duas consultas no primeiro trimestre, duas a três consultas no segundo trimestre e duas a seis consultas no terceiro trimestre. Somente três estudos incluíram avaliações puerperais. A rotina de exames preconizada apresenta mínimas variações entre os autores. Até o momento, não existem protocolos brasileiros validados para atendimento pré-natal por telemedicina. Os estudos incluídos evidenciaram a satisfação das gestantes em relação a esta forma de atendimento, e os desfechos de interesse, excetuando doenças hipertensivas, foi semelhante entre os grupos expostos ao pré-natal tradicional e ao pré-natal híbrido. Conclusão A diretriz apresentada contempla as recomendações do Ministério da Saúde para atendimento pré-natal de gestantes de baixo risco, reduz a exposição ao ambiente hospitalar e os custos de atendimento. Seu emprego em um ensaio clínico randomizado, a ser desenvolvido por este grupo, proporcionará dados de mundo real, relativos à segurança, efetividade, satisfação e custos.

Abstract Objective To develop a protocol for hybrid low-risk prenatal care adapted to Brazilian guidelines, merging reduced face-to-face consultations and remote monitoring. Methods The PubMed, Embase, and Cochrane Library databases were systematically searched on telemedicine and antenatal care perspectives and adaptation of the low-risk prenatal care protocols recommended by the Ministry of Health and by the Brazilian Federation of Gynecology and Obstetrics Associations. Results Five relevant articles and three manuals were included in the review, for presented criteria to develop this clinical guideline. We identified, in these studies, that the schedule of consultations is unevenly distributed among the gestational trimesters, and ranges from 7 to 14 appointments. In general, the authors propose one to two appointments in the first trimester, two to three appointments in the second trimester, and two to six appointments in the third trimester. Only three studies included puerperal evaluations. The routine exams recommended show minimal variations among authors. To date, there are no validated Brazilian protocols for prenatal care by telemedicine. The included studies showed that pregnant women were satisfied with this form of care, and the outcomes of interest, except for hypertensive diseases, were similar between the groups exposed to traditional and hybrid prenatal care. Conclusion The presented guideline comprises the Ministry of Health recommendations for low-risk prenatal care and reduces exposure to the hospital environment and care costs. A randomized clinical trial, to be developed by this group, will provide real-world data on safety, effectiveness, satisfaction, and costs.

ABSTRACT COVID-19 is a disease whose knowledge is still under construction, high transmissibility, with no consensual treatment available to everyone. Therefore, the identification of patients at higher risk of evolving to the critical form of the disease is fundamental. The study aimed to determine risk factors associated with the severity of COVID-19 in adults patients. This is an observational, retrospective study from a cohort of adult patients with COVID-19 admitted to a public hospital from March to August 2020, whose medical records were evaluated. For the association of possible severity predictors, a Poisson regression was used. The primary outcome was the critical form of the disease (need for admission to the Intensive Care Unit and/or invasive mechanical ventilation). We included 565 patients: mostly men; 55.5% of those who progressed to the critical form of the disease were over sixty years old. Hypertension, diabetes mellitus and obesity were the most frequent comorbidities. There were 39.8% of patients who progressed to the critical form of the disease. The hospital mortality rate was 22.1%, and that of critical patients was 46.7%. The independent factors associated with the severity of the disease were obesity [RR = 1.33 (95% CI 1.07 to 1.66; p = 0.011)], SpO2/FiO2 ratio ≤ 315 [RR = 2.20 (95% CI 1.79 to 2.71; p = 0.000)], C-reactive protein > 100 mg/L [RR = 1.65 (95% CI 1.33 to 2.06; p = 0.000)], and lymphocytes < 1,000/µL [RR = 1.44 (95% CI 1.18 to 1.75; p = 0.000)]. Advanced age and comorbidities were dependent factors strongly associated with the critical form of the disease.

ABSTRACT BACKGROUND: Despite widespread usage of central blood pressure assessment its predictive value among elderly people remains unclear. OBJECTIVE: To ascertain the capacity of central hemodynamic indices for predicting future all-cause and cardiovascular hard outcomes among elderly people. DESIGN AND SETTING: Systematic review and meta-analysis developed at the Del Cuore cardiology clinic, in Antonio Prado, Rio Grande do Sul, Brazil. METHODS: 312 full-text articles were analyzed, from which 35 studies were included for systematic review. The studies included needed to report at least one central hemodynamic index among patients aged 60 years or over. RESULTS: For all-cause mortality, aortic pulse wave velocity (aPWV) and central systolic blood pressure (SBP) were significant, respectively with standardized mean difference (SMD) 0.85 (95% confidence interval, CI 0.69-1.01; I2 96%; P < 0.001); and SMD 0.27 (95% CI 0.15-0.39; I2 77%; P 0.012). For cardiovascular mortality brachial-ankle PWV (baPWV), central SBP and carotid-femoral PWV (cfPWV) were significant, respectively SMD 0.67 (95% CI 0.40-0.93; I2 0%; P 0.610); SMD 0.65 (95% CI 0.48- 0.82; I2 80%; P 0.023); and SMD 0.51 (95% CI 0.32-0.69; I2 85%; P 0.010). CONCLUSIONS: The meta-analysis results showed that aPWV was promising for predicting all-cause mortality, while baPWV and central SBP demonstrated consistent results in evaluating cardiovascular mortality outcomes. Thus, the findings support usage of central blood pressure as a risk predictor for hard outcomes among elderly people. REGISTRATION NUMBER IN PROSPERO: RD42018085264

ABSTRACT Hepatitis A virus (HAV) infection has been considered one of the leading causes of acute hepatitis. The aim of the present study was to estimate the prevalence of HAV among children and adolescents in a population-based study in the capitals of the States of the North, Southeast and South of Brazil and identify predictive factors for the infection. A multi-stage sampling was used to select subjects aged between 5-9 and 10-19 years. Individual and household levels aside from the level of variables in the areas were collected. The outcome was the total IgG antibodies to HAV levels detected using a commercial Enzyme Immuno Assay (EIA). The associations between HAV and the independent variables were assessed using the odds ratio. A multilevel analysis was performed by GLLAMM using the Stata software. The prevalence of HAV infection in the 5-9 and 10-19 age groups was 28.7% and 67.5%, respectively for the North, 20.6% and 37.7%, for the Southeast and 18.9% and 34.5% for the South Region. The prevalence of HAV increased according to age in all sites. Variables related to education at the individual level (North and South), family and area level (South and Southeast) and family income level (Southeast and South) were independently associated with HAV infection. This emphasizes the need for individualized strategies to prevent the infection.

Abstract Introduction Specialized psychosocial care centers (Centros de Atenção Psicossocial [CAPS]) are mental health services focused on social rehabilitation and reducing hospitalization of patients with severe and persistent mental illness. Collective multiprofessional activities (CMPA) are the main therapeutic tools used at CAPS. This study aimed to determine rates of adherence to CMPA and identify factors associated with adherence. Methods This is a cross-sectional study in which 111 CAPS users were evaluated using questionnaires covering patient characteristics, clinical status, and treatment and incorporating the Functioning Assessment Short Test (FAST), the Clinical Global Impression – Severity scale (CGI-S), and the Clinical Global Impression – Improvement scale (CGI-I). Adherence was defined as attendance at 50% or more CMPA during the previous 3 months. Data were analyzed using descriptive statistics, bivariate analysis, and Poisson logistic regression with robust variance to estimate prevalence ratios. Results CPMA adherence was 43%. Having children aged 14 years or younger was significantly associated with non-adherence (71%, p = 0.001). Poor or partial adherence to psychotropic drugs tended to be associated (p = 0.066) with poor adherence (33% higher risk), as was the number of psychiatric hospitalizations during CAPS (p = 0.076), with a cumulative association of 5% non-adherence per hospitalization. Conclusions CMPA adherence was low in the study. It is necessary to consider the environment in which the individual lives and invest in support networks, providing patients and family members with explanations about the importance of CMPA to rehabilitation and attempting to tailor the care provided to each patient’s needs. There was an association between greater number of psychiatric hospitalizations and non-adherence, suggesting that CAPS are fulfilling a preventive role.

Objective: To describe the methodological characteristics and good research practices of the intervention studies for COVID-19 developed in Brazil in the first months of the pandemic. Method: A review in the CONEP-COVID bulletin (05/28/2020) and the International Clinical Trials Registry Platform, ClinicalTrials.gov, ReBEC was conducted to identify drug-type, biological therapy or vaccine intervention studies registered in Brazil. The studies were evaluated for methodological characteristics and power for different magnitudes of effect. Results: 62 studies were included, 55 identified on the CONEP website and seven on a registration database. Several interventions are being tested: chloroquine/hydroxychloroquine, azithromycin, plasma convalescent, tocilizumab, sarilumab, eculizumab, vaccine, corticoids, anticoagulants, n-acetylcysteine, nitazoxanide, ivermectin, lopinavir/ritonavir, etc. By May 2020, 22 research protocols were published in a protocol registry database, 82% were randomized clinical trials and 59% had adequate control group. However, 59% were not masked and only 24% included patients with positive test with diagnostic accuracy. Most of the studies would have power &gt;80% just to identify large effect sizes. In a prospective follow-up, until July 21st/2020, 60% of the studies available at CONEP were not registered in the ICTRP/ReBEC/ClinicalTrials platforms. Conclusion: The interventions evaluated during the Brazilian research response reflect international initiatives, but with a different distribution, a larger proportion of studies asseessed hydroxychloroquine/chloroquine. Limitations in methodological design and sample planning represent challenges that could affect the research outreach.

Objetivo: Descrever as características metodológicas e de boas práticas em pesquisa dos estudos de intervenção para COVID-19 desenvolvidos no Brasil nos primeiros meses da pandemia. Método: Revisamos o boletim da CONEP-COVID (28/05/2020) e as bases International Clinical Trials Registry Platform, ClinicalTrials.gov e ReBEC para identificar estudos registrados no Brasil, avaliando intervenções de tipo medicamento, terapia biológica ou vacinas. Descrevemos as características metodológicas e calculamos o poder para diferentes magnitudes de efeito. Resultados: Foram incluídos 62 estudos, 55 identificados no site da CONEP e mais sete nas bases de registro. As intervenções medicamentosas mais frequentemente testadas nesses estudos foram: cloroquina/hidroxicloroquina, azitromicina, plasma convalescente, tocilizumabe, sarilumabe, eculizumabe, vacina, corticoides, anticoagulantes, n-acetilcisteína, nitazoxanida, ivermectina e lopinavir/ritonavir. De 22 protocolos, publicados até maio de 2020 nas bases de registro, 18 (82%) eram ensaios clínicos randomizados e 13 (59%) tinham grupo controle adequado. Entretanto, nove (41%) eram mascarados e somente cinco (24%) incluía pacientes diagnosticados com teste de laboratório específico (e.g. RT-PCR). A maioria desses trabalhos teria poder &gt;80% apenas para identificar grandes tamanhos de efeito. Em seguimento prospectivo, observamos que 60% dos estudos, disponíveis na CONEP até maio de 2020, não estava em nenhuma das plataformas de registro (ICTRP/ReBEC/ClinicalTrials) até o dia 21/07/2020.Conclusão: As intervenções avaliadas durante a resposta brasileira em pesquisa refletem iniciativas internacionais, porém com uma distribuição diferente, tendo um número elevado de estudos avaliando hidroxicloroquina/ cloroquina. Limitações no delineamento metodológico e planejamento amostral representam desafios que poderiam afetar o alcance dos trabalhos.

Abstract Introduction Coronavirus disease 19 (COVID-19) is potentially the greatest global public health crisis of this century. This disease emerged as an outbreak of pneumonia of unknown cause in Wuhan, the capital city of the Hubei province in China, in December 2019. Otolaryngologists, head and neck surgeons and dentists are at an increased risk of occupational disease. Objective The present review summarizes currently published evidence of Covid-19 epidemiology, clinical characteristics, treatment and prevention. No proven effective treatments for this disease currently exist. Data Synthesis COVID-19 started from a zoonotic transmission event associated with a large seafood market that also traded in live wild animals, and it soon became clear that efficient person-to-person transmission was also occurring. Symptoms are varied, and not all patients develop all of them. Conclusion Social distancing seems to have been successful in several places in the world. However, this recommendation alone is not enough to contain the disease, and it is not a long-term solution. Large-scale testing by health professionals of representative samples of the population may give an estimate of the progression of the disease. Different treatments are under test and bring hope of a cure to the population. However, no current treatments (April 27, 2020) have been proven to be the key to success in the treatment of patients with COVID-19. Planetary health is a useful concept to understand the current drivers of this pandemic and to draw a roadmap for science and healthcare that may guide actions to fight economic depression and ensure a healthy recovery.

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Clinical trials investigating COVID-19 treatments have been carried out worldwide, and also in Brazil. Public registration of research protocols promotes transparency and follows the legislation. Objective: To describe the research protocols under development in Brazil for COVID-19 treatment, and to evaluate the publication after registration in Plataforma Brasil/CONEP. Method: A comprehensive search on the databases ClinicalTrials.gov, ReBEC (Brazilian database) and the International Clinical Trials Registry Platform, was performed on April 11, 2020. The CONEP Bulletin was consulted on April 7, 2020 to assess intervention studies elected for public registration. Results: Our study shows that there are sixteen intervention studies in progress about COVID-19 in Brazil (CONEP registry), however, only six are available in the protocol registry databases. Brazilian open access research protocols investigated interventions with: hydroxychloroquine associated with azithromycin or monotherapy; chloroquine; dexamethasone; mesenchymal stem cells. Five studies (83%) are open and include patients with suspected or confirmed COVID-19 diagnosis. Three studies are multicenter and all include clinically relevant outcomes. Conclusion: Brazil has research protocols in progress for COVID-19 treatment, however only 1/3 of these studies has public protocols available.

As pesquisas clínicas relacionadas a COVID-19 estão acontecendo em todo o mundo, inclusive no Brasil. Os registros públicos de protocolos de pesquisa promovem transparência e atendem às regulamentações. Objetivo: Descrever os protocolos de pesquisa em desenvolvimento no Brasil para investigações de alternativas terapêuticas para a COVID-19, e avaliar o cumprimento da publicação após registro na Plataforma Brasil/CONEP. Método: Uma revisão foi realizada nas bases de registro de protocolos de pesquisa: ClinicalTrials.gov,&nbsp; ReBEC (base brasileira) e International Clinical Trials Registry Platform, no dia 11 de abril de 2020. O boletim da CONEP do dia 07 de abril de 2020 foi consultado para avaliar estudos de intervenção passíveis de registro. Resultados: Nosso estudo mostrou que há dezesseis estudos de intervenção sobre a COVID-19 em andamento no Brasil e registrados na CONEP, porém somente seis estão disponíveis nas bases de registro de protocolos. Os protocolos brasileiros de acesso aberto incluíram a investigação de intervenções com: hidroxicloroquina associada a azitromicina ou em monoterapia; cloroquina; dexametasona; células tronco mesenquimais. Cinco estudos (83%) são abertos e incluíram pacientes com suspeita ou diagnóstico confirmado da COVID-19. Três estudos são multicêntricos e todos propõe a avaliação de desfechos clinicamente relevantes. Conclusão: O Brasil tem protocolos de pesquisa em andamento de intervenções terapêuticas na COVID-19, porém apenas 1/3 destes estudos têm protocolos públicos.

Resumo Este artigo visa investigar fatores associados à maternidade em adolescentes moradoras em Porto Alegre, com idade entre 14 e 16 anos. Estudo caso-controle com 431 mães adolescentes (casos) e 862 adolescentes que nunca tiveram filho (controles). Os dados foram obtidos através de visitas domiciliares e mediante aplicação de questionário estruturado. Foram estudadas variáveis sociodemográficas, qualidade das relações sociais e familiares, estilo de vida e histórico de abuso como possíveis determinantes da maternidade na adolescência. Os dados foram analisados através de regressão logística condicional, utilizando modelo hierarquizado. Os resultados mostram que pertencer aos estratos socioeconômicos mais baixos, possuir mãe que teve o seu primeiro filho até os 19 anos, defasagem escolar, uso de tabaco e embriaguez alcóolica pelo menos uma vez na vida foram positivamente associados à maternidade na adolescência. Menarca mais tardia e ter familiares ou ter amigos em quem confiar foram fatores de proteção. Defasagem escolar, que obteve razão de odds mais elevada, aponta o importante papel da escola na formação desta população e no seu potencial para estimular hábitos de vida sadios.

Abstract This paper investigates factors associated with motherhood among adolescents from 14 to 16 years of age in Porto Alegre, Brazil. This is a case-control study with 431 adolescent mothers (cases) and 862 adolescents who had never given birth (controls). D. ata were obtained through home visits by an interviewer-applied questionnaire. Sociodemographic characteristics, quality of social and family relationships, lifestyle and history of abuse were studied as potential determinants to early adolescent motherhood. Conditional logistic regression was used for data analysis according to a two-stage hierarchical model. Results showed that lower economic class, schooling failure, tobacco consumption, alcoholic drunkenness at least once in life and having a mother who gave birth before 20 years of age were positively associated with early adolescent motherhood. Later menarche and having relatives or having friends in whom to trust remained as protective factors. Schooling failure, which obtained the highest risk, points to the important role of the school in this population’s development and its potential to stimulate healthy life habits.

Resumo Este artigo visa investigar fatores associados à maternidade em adolescentes moradoras em Porto Alegre, com idade entre 14 e 16 anos. Estudo caso-controle com 431 mães adolescentes (casos) e 862 adolescentes que nunca tiveram filho (controles). Os dados foram obtidos através de visitas domiciliares e mediante aplicação de questionário estruturado. Foram estudadas variáveis sociodemográficas, qualidade das relações sociais e familiares, estilo de vida e histórico de abuso como possíveis determinantes da maternidade na adolescência. Os dados foram analisados através de regressão logística condicional, utilizando modelo hierarquizado. Os resultados mostram que pertencer aos estratos socioeconômicos mais baixos, possuir mãe que teve o seu primeiro filho até os 19 anos, defasagem escolar, uso de tabaco e embriaguez alcóolica pelo menos uma vez na vida foram positivamente associados à maternidade na adolescência. Menarca mais tardia e ter familiares ou ter amigos em quem confiar foram fatores de proteção. Defasagem escolar, que obteve razão de odds mais elevada, aponta o importante papel da escola na formação desta população e no seu potencial para estimular hábitos de vida sadios.

Abstract This paper investigates factors associated with motherhood among adolescents from 14 to 16 years of age in Porto Alegre, Brazil. This is a case-control study with 431 adolescent mothers (cases) and 862 adolescents who had never given birth (controls). D. ata were obtained through home visits by an interviewer-applied questionnaire. Sociodemographic characteristics, quality of social and family relationships, lifestyle and history of abuse were studied as potential determinants to early adolescent motherhood. Conditional logistic regression was used for data analysis according to a two-stage hierarchical model. Results showed that lower economic class, schooling failure, tobacco consumption, alcoholic drunkenness at least once in life and having a mother who gave birth before 20 years of age were positively associated with early adolescent motherhood. Later menarche and having relatives or having friends in whom to trust remained as protective factors. Schooling failure, which obtained the highest risk, points to the important role of the school in this population’s development and its potential to stimulate healthy life habits.

RESUMO: Objetivo: Descrever as características metodológicas e de boas práticas em pesquisa dos estudos de intervenção para COVID-19 desenvolvidos no Brasil nos primeiros meses da pandemia. Métodos: Revisamos o boletim da Comissão Nacional de Ética em Pesquisa - edição especial Coronavírus (CONEP-COVID) (28 de maio de 2020) e as bases International Clinical Trials Registry Platform (ICTRP), ClinicalTrials.gov e Registro Brasileiro de Ensaios Clínicos (ReBEC) para identificar estudos registrados no Brasil que avaliassem intervenções de tipo de medicamento, terapia biológica ou vacinas. Descrevemos as características metodológicas e calculamos o poder para diferentes magnitudes de efeito. Resultados: Foram incluídos 62 estudos, 55 identificados no site da CONEP e mais sete nas bases de registro. As intervenções medicamentosas mais frequentemente testadas nesses estudos foram: cloroquina/hidroxicloroquina, azitromicina, plasma convalescente, tocilizumabe, sarilumabe, eculizumabe, vacina, corticoides, anticoagulantes, n-acetilcisteína, nitazoxanida, ivermectina e lopinavir/ritonavir. De 22 protocolos publicados até maio de 2020 nas bases de registro, 18 (82%) eram ensaios clínicos randomizados e 13 (59%) tinham grupo controle adequado. Entretanto, nove (41%) eram mascarados e somente cinco (24%) incluíam pacientes diagnosticados com teste de laboratório específico (por exemplo, transcrição reversa seguida de reação em cadeia da polimerase - RT-PCR). A maioria desses trabalhos teria poder > 80% apenas para identificar grandes tamanhos de efeito. Em seguimento prospectivo, observamos que 60% dos estudos disponíveis na CONEP até maio de 2020 não estavam em nenhuma das plataformas de registro (ICTRP/ReBEC/ClinicalTrials) até o dia 21 de julho de 2020. Conclusão: As intervenções avaliadas durante a resposta brasileira em pesquisa refletem iniciativas internacionais, porém com distribuição diferente, tendo número elevado de estudos que avaliam hidroxicloroquina/cloroquina. Limitações no delineamento metodológico e planejamento amostral representam desafios que podem afetar o alcance dos trabalhos.

ABSTRACT: Objective: To describe the methodological characteristics and good research practices of COVID-19 interventional studies developed in Brazil in the first months of the pandemic. Methods: We reviewed the bulletin of the National Research Ethics Committee - Coronavirus Special Edition (Comissão Nacional de Ética em Pesquisa - CONEP-COVID) (May 28, 2020) and the databases of the International Clinical Trials Registry Platform (ICTRP), ClinicalTrials.gov, and Brazilian Clinical Trials Registry (Registro Brasileiro de Ensaios Clínicos - ReBEC) to identify interventional studies registered in Brazil that assessed drug type, biological therapy, or vaccines. We described their methodological characteristics and calculated their power for different effect magnitudes. Results: A total of 62 studies were included, 55 retrieved from the CONEP website, and 7 from registry databases. The most tested pharmacological interventions in these studies were: chloroquine/hydroxychloroquine, azithromycin, convalescent plasma, tocilizumab, sarilumab, eculizumab, vaccine, corticosteroids, anticoagulants, n-acetylcysteine, nitazoxanide, ivermectin, and lopinavir/ritonavir. Out of 22 protocols published on registry databases until May 2020, 18 (82%) were randomized clinical trials, and 13 (59%) had an appropriate control group. However, 9 (41%) of them were masked, and only 5 (24%) included patients diagnosed with a specific laboratory test (for example, reverse transcription polymerase chain reaction - RT-PCR). Most of these studies had power > 80% only to identify large effect sizes. In the prospective follow-up, 60% of the studies available at CONEP until May 2020 had not been published on any registry platform (ICTRP/ReBEC/ClinicalTrials) by July 21, 2020. Conclusion: The interventions evaluated during the Brazilian research response reflect those of international initiatives, but with a different distribution and a large number of studies assessing hydroxychloroquine/chloroquine. Limitations in methodological design and sample planning represent challenges that could affect the research outreach.

RESUMO: Objetivo: Descrever as características metodológicas e de boas práticas em pesquisa dos estudos de intervenção para COVID-19 desenvolvidos no Brasil nos primeiros meses da pandemia. Métodos: Revisamos o boletim da Comissão Nacional de Ética em Pesquisa - edição especial Coronavírus (CONEP-COVID) (28 de maio de 2020) e as bases International Clinical Trials Registry Platform (ICTRP), ClinicalTrials.gov e Registro Brasileiro de Ensaios Clínicos (ReBEC) para identificar estudos registrados no Brasil que avaliassem intervenções de tipo de medicamento, terapia biológica ou vacinas. Descrevemos as características metodológicas e calculamos o poder para diferentes magnitudes de efeito. Resultados: Foram incluídos 62 estudos, 55 identificados no site da CONEP e mais sete nas bases de registro. As intervenções medicamentosas mais frequentemente testadas nesses estudos foram: cloroquina/hidroxicloroquina, azitromicina, plasma convalescente, tocilizumabe, sarilumabe, eculizumabe, vacina, corticoides, anticoagulantes, n-acetilcisteína, nitazoxanida, ivermectina e lopinavir/ritonavir. De 22 protocolos publicados até maio de 2020 nas bases de registro, 18 (82%) eram ensaios clínicos randomizados e 13 (59%) tinham grupo controle adequado. Entretanto, nove (41%) eram mascarados e somente cinco (24%) incluíam pacientes diagnosticados com teste de laboratório específico (por exemplo, transcrição reversa seguida de reação em cadeia da polimerase - RT-PCR). A maioria desses trabalhos teria poder > 80% apenas para identificar grandes tamanhos de efeito. Em seguimento prospectivo, observamos que 60% dos estudos disponíveis na CONEP até maio de 2020 não estavam em nenhuma das plataformas de registro (ICTRP/ReBEC/ClinicalTrials) até o dia 21 de julho de 2020. Conclusão: As intervenções avaliadas durante a resposta brasileira em pesquisa refletem iniciativas internacionais, porém com distribuição diferente, tendo número elevado de estudos que avaliam hidroxicloroquina/cloroquina. Limitações no delineamento metodológico e planejamento amostral representam desafios que podem afetar o alcance dos trabalhos.

ABSTRACT: Objective: To describe the methodological characteristics and good research practices of COVID-19 interventional studies developed in Brazil in the first months of the pandemic. Methods: We reviewed the bulletin of the National Research Ethics Committee - Coronavirus Special Edition (Comissão Nacional de Ética em Pesquisa - CONEP-COVID) (May 28, 2020) and the databases of the International Clinical Trials Registry Platform (ICTRP), ClinicalTrials.gov, and Brazilian Clinical Trials Registry (Registro Brasileiro de Ensaios Clínicos - ReBEC) to identify interventional studies registered in Brazil that assessed drug type, biological therapy, or vaccines. We described their methodological characteristics and calculated their power for different effect magnitudes. Results: A total of 62 studies were included, 55 retrieved from the CONEP website, and 7 from registry databases. The most tested pharmacological interventions in these studies were: chloroquine/hydroxychloroquine, azithromycin, convalescent plasma, tocilizumab, sarilumab, eculizumab, vaccine, corticosteroids, anticoagulants, n-acetylcysteine, nitazoxanide, ivermectin, and lopinavir/ritonavir. Out of 22 protocols published on registry databases until May 2020, 18 (82%) were randomized clinical trials, and 13 (59%) had an appropriate control group. However, 9 (41%) of them were masked, and only 5 (24%) included patients diagnosed with a specific laboratory test (for example, reverse transcription polymerase chain reaction - RT-PCR). Most of these studies had power > 80% only to identify large effect sizes. In the prospective follow-up, 60% of the studies available at CONEP until May 2020 had not been published on any registry platform (ICTRP/ReBEC/ClinicalTrials) by July 21, 2020. Conclusion: The interventions evaluated during the Brazilian research response reflect those of international initiatives, but with a different distribution and a large number of studies assessing hydroxychloroquine/chloroquine. Limitations in methodological design and sample planning represent challenges that could affect the research outreach.