Do movement behaviours influence the association between early menarche and depression symptoms among Brazilian adolescents?
Bibliographic record
Abstract
Early menarche has been associated with adverse health outcomes, such as depressive symptoms. Discovering effect modifiers across these conditions in the pediatric population is a constant challenge. We tested whether movement behaviours modified the effect of the association between early menarche and depression symptoms among adolescents. This cross-sectional study included 2031 females aged 15–19 years across all Brazilian geographic regions. Data were collected using a self-administered questionnaire; 30.5% ( n = 620) reported having experienced menarche before age 12 years (i.e., early menarche). We used the Patient Health Questionnaire (PHQ-9) to evaluate depressive symptoms. Accruing any moderate–vigorous physical activity during leisure time, limited recreational screen time, and having good sleep quality were the exposures investigated. Adolescents who experienced early menarche and met one ( B: –4.45, 95% CI: (–5.38, –3.51)), two ( B: –6.07 (–7.02, –5.12)), or three ( B: –6.49 (–7.76, –5.21)), and adolescents who experienced not early menarche and met one ( B: –5.33 (–6.20; –4.46)), two ( B: –6.12 (–6.99; –5.24)), or three ( B: –6.27 (–7.30; –5.24)) of the movement behaviour targets had lower PHQ-9 scores for depression symptoms than adolescents who experienced early menarche and did not meet any of the movement behaviours. The disparities in depressive symptoms among the adolescents (early menarche vs. not early menarche) who adhered to all three target behaviours were not statistically significant ( B: 0.41 (–0.19; 1.01)). Adherence to movement behaviours modified the effect of the association between early menarche and depression symptoms.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".