The Influence of Female Reproductive Factors on Longevity: A Systematized Narrative Review of Epidemiological Studies
Bibliographic record
Abstract
Purpose: This systematized review presents a synthesis of epidemiological studies that examine the association between female reproductive factors and longevity indicators. Methods: A comprehensive literature search was conducted using four bibliographic databases: OVID Medline, Web of Science, PubMed, and Google Scholar, including English language articles published until March 2022. Results from the search strategy yielded 306 articles, 37 of which were included for review based on eligibility criteria. Results were identified within the following nine themes: endogenous androgens and estrogens, age at first childbirth, age at last childbirth, parity, reproductive lifespan, menopause-related factors, hormone therapy use, age at menarche, and offspring gender. Results: Evidence that links reproductive factors and long lifespan is limited. Several female reproductive factors are shown to be significantly associated with longevity, yet findings remain inconclusive. The most consistent association was between parity (fertility and fecundity) and increased female lifespan. Age at first birth and parity were consistently associated with increased longevity. Associations between age at menarche and menopause, premature menopause, reproductive lifespan, offspring gender and longevity are inconclusive. Conclusion: There is not enough evidence to consider sex a longevity predictor. To understand the mechanisms that predict longevity outcomes, it is imperative to consider sex-specific within-population differences.
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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".