Excess weight and obesity prevalence in the RPS Brazilian Birth Cohort Consortium (Ribeirão Preto, Pelotas and São Luís)
Bibliographic record
Abstract
Our objective was to estimate the prevalence of excess weight and obesity, according to sex and income in the RPS Brazilian Birth Cohort Consortium (Ribeirão Preto, Pelotas, and São Luís). Participants in the Ribeirão Preto (1978/1979 and 1994), Pelotas (1982, 1993 and 2004) and São Luís (1997/1998) birth cohorts were included in different follow-ups from 7 years old onwards. Excess weight (overweight and obesity) were assessed by body mass index. The highest prevalences were observed in Ribeirão Preto (excess weight: 27.7% at 9-11 and 47.1% at 22-23 years; obesity: 8.6% at 9-11 and 19.8% at 22-23 years) while the smallest was in São Luís (excess weight: 5.4 to 7-9 and 17.2% at 18-19 years; obesity: 1.8% at 7-9 and 3.6% at 18-19 years). The younger the cohort, the greater the prevalence of excess weight and obesity when comparing similar age groups. Increases in obesity prevalence were greater than in excess weight prevalence. Women had lower excess weight prevalence in older cohorts and higher obesity prevalence in younger cohorts. Higher excess weight and obesity prevalence were observed in higher income children and adolescents, and in poorer adults. Differences in the prevalence of excess weight and obesity evidenced that individuals from younger cohorts are more exposed to this morbidity, as well as those who were born in the most developed city, low-income adults as well as children and adolescents belonging to families of the highest income tertile. Therefore, the results of this study indicate the need to prioritize actions aimed at younger individuals.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".