Ambient Temperature and Risk of Preeclampsia: Biased Association?
Bibliographic record
Abstract
BACKGROUND: Preeclampsia is associated with conception during warm months and delivery during cold months. We sought to determine whether season of conception and shorter gestation bias the associations. METHODS: We used hospital discharge summaries to identify 65 273 pregnancies with and 1 825 438 without preeclampsia in Quebec, Canada between 1989 and 2012. We obtained data on mean temperature for the month following conception and the month before hospital admission. We used cubic splines in log-binomial models to estimate the association between temperature and preeclampsia (risk ratios, RR; 95% confidence interval, CI). To assess the potential for bias, we compared models progressively adjusted for baseline maternal characteristics, season of conception, and length of gestation at admission. RESULTS: With adjustment for baseline maternal characteristics only, risk of preeclampsia was higher for hot temperatures at conception and cold temperatures at end of pregnancy. Adjusting for season at conception removed the association between preeclampsia and temperature at conception. Adjustment for length of gestation removed the association between preeclampsia and temperature at end of pregnancy. CONCLUSIONS: This study demonstrates that associations between ambient temperature and preeclampsia may be biased by short gestation, because preeclampsia commonly occurs earlier in pregnancy. Temperatures during gestation change with time for all women, and temperatures early in pregnancy frequently differ from temperatures later in pregnancy. Variation in temperature over gestation may lead to a coincidental association with preeclampsia.
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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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".