Climate Change and Congenital Anomalies: A Population‐Based Study of the Effect of Prolonged Extreme Heat Exposure on the Risk of Neural Tube Defects in France
Bibliographic record
Abstract
BACKGROUND: Exposure to long-lasting extreme ambient temperatures in the periconceptional or early pregnancy period might increase the risk of neural tube defects (NTDs). We tested whether prolonged severe heat exposure as experienced during the 2003 extreme heatwave in France, affected the risk of NTDs. METHODS: We retrieved NTD cases spanning from January 1994 to December 2018 from the Paris Registry of Congenital Malformations. The 2003 heatwave was characterized by the long duration and high intensity of nine consecutive days with temperatures ≥35°C. We classified monthly conceptions occurring in August 2003 as "exposed" to prolonged extreme heat around conception (i.e., periconceptional period). We assessed whether the risk of NTDs among cohorts exposed to the prolonged severe heatwave of 2003 in the periconceptional period differed from expected values using Poisson/negative binomial regression. FINDINGS: We identified 1272 NTD cases from January 1994 to December 2018, yielding a monthly mean count of 4.24. Ten NTD cases occurred among births conceived in August 2003. The risk of NTD was increased in the cohort with periconceptional exposure to the August 2003 heatwave (relative risk = 2.14, 95% confidence interval: 1.46 to 3.13), compared to non-exposed cohorts. Sensitivity analyses excluding July and September months or restricting to summer months yielded consistent findings. INTERPRETATION: Evidence from the "natural experiment" of an extreme climate event suggests an elevated risk of NTDs following exposure to prolonged extreme heat during the periconceptional period.
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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) | 0.000 | 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 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".