Maternal exposure to metals and time‐to‐pregnancy: The <scp>MIREC</scp> cohort study
Bibliographic record
Abstract
OBJECTIVE: To study the association between maternal exposure to arsenic, cadmium, lead, manganese and mercury, time-to-pregnancy (TTP) and infertility. DESIGN: Pregnancy-based retrospective TTP cohort study. SETTING: Hospitals and clinics from ten cities across Canada. POPULATION: A total of 1784 pregnant women. METHODS: Concentrations of arsenic, cadmium, lead, manganese and mercury were measured in maternal whole blood during the first trimester of pregnancy as a proxy of preconception exposure. Discrete-time Cox proportional hazards models generated fecundability odds ratios (FOR) for the association between metals and TTP. Logistic regression generated odds ratios (OR) for the association between metals and infertility. Models were adjusted for maternal age, pre-pregnancy body mass index, education, income, recruitment site and plasma lipids. MAIN OUTCOME MEASURES: TTP was self-reported as the number of months of unprotected intercourse to become pregnant. Infertility was defined as TTP longer than 12 months. RESULTS: A total of 1784 women were eligible for the analysis. Mean ± SD maternal age and gestational age at interview were 32.2 ± 5.0 years, and 11.6 ± 1.6 weeks, respectively. Exposure to arsenic, cadmium, manganese or mercury was not associated with TTP or infertility. Increments of one standard deviation of lead concentrations resulted in a shorter TTP (adjusted FOR 1.09, 95% CI 1.02-1.16); however, the association was not linear when exposure was modelled in tertiles. CONCLUSION: Blood concentrations of metals at typical levels of exposure among Canadian pregnant women were not associated with TTP or infertility. Further studies are needed to assess the role of lead, if any, on TTP.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".