The association between age at menarche and depression: A systematic review and meta-analysis with meta-regression
Bibliographic record
Abstract
INTRODUCTION: The existing literature presents conflicting findings regarding the relationship between Age At Menarche (AAM) and depression. Thus, to address this gap, this systematic review and meta-analysis aimed to evaluate current evidence to clarify the association between AAM and depression. METHODS: Medline (PubMed), Scopus, Embase, Web of Science, and Google Scholar were searched from 2000 until June 2024 to include cross-sectional, case-control, and cohort studies. The quality of the evidence was assessed using the Newcastle-Ottawa Scale (NOS) instrument. The odds ratio (OR) of depression and its 95 % Confidence Interval (95 % CI) were calculated using the random effects model and inverse variance method. The protocol is registered in PROSPERO, number CRD42024551838. RESULTS: From a total of 2175 search records, 13 studies were included comprising 434,838 participants with NOS scores ranging from 7 to 9. The present findings showed that early menarche is associated with significantly higher odds of depression compared to both normative AAM (OR = 1.36, 95 % CI: 1.20‒1.53) and late AAM (OR = 1.52, 95 % CI 1.22‒1.90). Also, females with later menarche had lower odds of depression compared to females with normal AAM (OR = 0.91, 95 % CI 0.76‒1.09); however, this association was not statistically significant. CONCLUSION: The present findings demonstrated that early menarche is associated with elevated odds of depression compared to females of both normative AAM and late AAM.
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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 | 0.017 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.040 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".