Cohort Profile: Brazilian High-Risk Cohort for Mental Health Conditions (BHRC)
Bibliographic record
Abstract
The Brazilian High-Risk Cohort for Mental Health Conditions (BHRC), formerly the High-Risk Cohort Study for Psychiatric Disorders in Childhood (HRC) [1], was established to advance the understanding of the developmental trajectories of pediatric mental health conditions by integrating information about behaviors, genes, environments, and brain development. The BHRC has an accelerated school-based longitudinal design in which children born between 1996 and 2004 were recruited at school in 2009 and 2010 and assessed every 2–4 years. The BHRC is one of the few population neuroscience studies from a middle-income country, integrating in-depth clinical assessments with data on environmental influences, genetics, imaging, cognition, and ecological momentary assessment (EMA), among others [2]. The cohort was established as a collaborative effort among three Brazilian universities: Universidade de São Paulo, Universidade Federal do Rio Grande do Sul, and Universidade Federal de São Paulo. This was possible through a networking grant from the Brazilian Science and Technology Ministry, which created the National Institute of Developmental Psychiatry for Children and Adolescents (in Portuguese, “Instituto Nacional de Psiquiatria do Desenvolvimento para a Infância e Adolescência”). Participants were recruited at state-funded schools of two Brazilian metropolitan areas: Porto Alegre (South, 1.5 million inhabitants, metropolitan area 4.4 million inhabitants) and São Paulo (Southeast, 11.4 million inhabitants, metropolitan area 22.8 million inhabitants).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".