Pregnancy outcome following gestational exposure to azithromycin
Bibliographic record
Abstract
BACKGROUND: Azithromycin is an azalide antibiotic with an extensive range of indications and has become a common treatment option due to its convenient dosing regimen and therapeutic advantages. Human studies addressing gestational use of azithromycin have primarily focused on antibiotic efficacy rather than fetal safety. Our primary objective was to evaluate the possibility of teratogenic risk following gestational exposure to azithromycin. METHODS: There were 3 groups of pregnant women enrolled in our study: 1) women who took azithromycin, 2) women exposed to non-teratogenic antibiotics for similar indications, and 3) women exposed to non-teratogenic agents. They were matched for gestational age at time of call, maternal age, cigarette and alcohol consumption. Rates of major malformations and other endpoints of interest were compared among the three groups. RESULTS: Pregnancy outcome of 123 women in each group was ascertained. There were no statistically significant differences among the three groups in the rates of major malformations; 3.4% (exposed) versus 2.3% (disease matched) and 3.4% (non teratogen) or any other endpoints that were examined. In the azithromycin group, 88 (71.6%) women took the drug during the first trimester CONCLUSION: Results suggest that gestational exposure to azithromycin is not associated with an increase in the rate of major malformations above the baseline of 1-3%. Our data adds to previous research showing that macrolide antibiotics, as a group, are generally safe in pregnancy and provides an evidence-based option for health professionals caring for populations with chlamydia.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".