Exposure to trimethoprim/sulfamethoxazole but not other FDA category C and D anti-infectives is associated with increased risks of preterm birth and low birth weight
Bibliographic record
Abstract
OBJECTIVE: To examine the association between trimethoprim/sulfamethoxazole, other US Food and Drug Administration (FDA) C and D anti-infectives, and non anti-infective FDA C, D, and X drugs used during pregnancy with preterm birth and low birth weight. METHODS: We carried out a retrospective cohort study based on a 50% random sample of women who gave birth in the Canadian province of Saskatchewan from 1997 to 2000. The association between trimethoprim/sulfamethoxazole, other FDA C and D anti-infectives (fluconazole, clarithromycin, doxycycline, and tetracycline), and non anti-infective FDA C, D, and X drugs used during pregnancy with preterm birth and low birth weight was evaluated using multiple logistic regression, with adjusted odds ratios (aORs) and 95% confidence intervals (CIs) as association measures. RESULTS: A total of 17 939 women were included in the final analysis. Trimethoprim/sulfamethoxazole was associated with significantly increased risks for preterm birth (aOR 1.51, 95% CI 1.10, 2.08) and low birth weight (aOR 1.67, 95% CI 1.14, 2.46). Exposure to non anti-infective FDA category C, D and X drugs was also associated with increased risks for preterm birth (aOR 1.17, 95% CI 1.09, 1.31) and low birth weight (aOR 1.14, 95% CI 0.92, 1.42), but to a lesser degree. Other FDA C and D anti-infectives were not (statistically) significantly associated with increased risks for preterm birth (aOR 0.93, 95% CI 0.49, 1.77) or low birth weight (aOR 0.65, 95% CI 0.27, 1.60). CONCLUSIONS: Among FDA C, D and X drugs, trimethoprim/sulfamethoxazole, a folic acid antagonist, has the strongest association with preterm birth and low birth weight.
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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 | 0.003 |
| 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 | 0.000 | 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".