The Safety of H<sub>2</sub>‐Blockers Use During Pregnancy
Bibliographic record
Abstract
Little data exist on the safety of H(2)-blockers during pregnancy. A computerized database of medications dispensed from 1998 to 2007 to all women registered in the "Clalit" health maintenance organization, in the Southern District of Israel, was linked with computerized databases containing maternal and infant hospitalization records from the district hospital. The following confounders were controlled for: parity, maternal age, ethnic group, maternal diabetes, smoking, and peripartum fever. Also, therapeutic pregnancy termination data were analyzed. A total of 117 960 infants were born during the study period, 84 823 of them (72%) to women registered at Clalit; 1148 of the latter were exposed to H(2)-blockers during the first trimester of pregnancy. Exposure to H(2)-blockers was not associated with an increased risk for congenital malformations (adjusted odds ratio [OR] = 1.03, 95% confidence interval [CI]: 0.80-1.32); also, no such association was found when therapeutic pregnancy terminations were included in the analysis (adjusted OR = 1.17, 95% CI: 0.93-1.46). Exposure to H(2)-blockers was not associated with perinatal mortality, premature delivery, low birth weight, or low Apgar scores.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.001 | 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".