Prenatal exposures to persistent organic pollutants and menstrual characteristics in girls at age 12 in the French PELAGIE cohort
Bibliographic record
Abstract
Few studies have evaluated the impact of prenatal exposure to persistent organic pollutants (POPs) on menstruation characteristics. Therefore, the aim of our study was to investigate the associations between cord blood concentrations of POPs and menstruation characteristics including menarche status, menstrual regularity, pain and duration in girls at age 12. We used data from the French PELAGIE mother-child cohort follow-up at 12 years old which included a questionnaire on menstruation characteristics. POP concentrations of 14 organochlorine pesticides (OCPs), 17 polychlorinated biphenyls (PCBs), 5 polybrominated diphenyl ethers (PBDEs), and 9 per-polyfluoroalkyl substances (PFAS) were measured on cord blood sampled at birth. We used multivariate logistic regression and Poisson regression to study the relation between prenatal POPs and menarche status, regularity of menstruations, menstruation duration, and severe pain in the lower abdomen or cramps (N = 296). The effect of mixtures of POPs on these outcomes were assessed using quantile g-computation. The majority of POPs were associated with a non-statistically significant increased risk of menarche at 12 years old, especially for the PCB mixture (OR = 1.29, 95 % CI [0.99; 1.68]). We also observed a statistically significant association between the second tercile of dieldrin (OR = 2.91, 95 % CI [1.13; 7.82]) and PFNA (OR = 3.13, 95 % CI [1.01; 10.43]) and severe pain in the lower abdomen or cramps, and a statistically significant association between the second tercile of hexachlorobenzene and menstruation duration (% change = 25.6 %, 95 % CI [1.2; 56.6]). For others POPs and other menstrual characteristics we did not observe any associations. Further longitudinal studies are needed to confirm these results, possibly through combined analyses of several mother-child cohorts to increase study power.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 | 0.000 |
| 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".