Maternal Hyperthermia and the Risk for Neural Tube Defects in Offspring
Bibliographic record
Abstract
BACKGROUND: In animals, excessive core body temperatures have been documented to cause malformations; neural tube defects (NTDs) are among the most frequently reported. In humans, data are inconclusive and often conflicting. The objective of our report is to determine the risk for neural tube defects associated with maternal hyperthermia in early pregnancy. METHODS: We conducted a systematic review and meta-analysis to evaluate available evidence on this topic in humans. MEDLINE, EMBASE, references from published reports, and biologic abstracts from meetings were searched for relevant studies. Reviewers evaluated all the retrieved articles and extracted the relevant data. Individual and summary odds ratios and relative risks were calculated using the Mantel-Haenszel method. RESULTS: Fifteen studies, reporting on 1,719 cases and 37,898 noncases, were included in the meta-analysis. The overall odds ratio for neural tube defects associated with maternal hyperthermia was 1.92 (95% confidence interval = 1.61-2.29). When analyzed separately, the 9 case-control studies had an odds ratio of 1.93 (1.53-2.42). The summary relative risk for the 6 cohort studies was 1.95 (1.30-2.92). CONCLUSIONS: Maternal hyperthermia in early pregnancy is associated with increased risk for neural tube defects and may be a human teratogen.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".