Use of trimethoprim–sulfamethoxazole during pregnancy and risk of spontaneous abortion: a nested case control study
Bibliographic record
Abstract
AIMS: Data available on the fetal safety of trimethoprim-sulfamethoxazole (TMP-SMX) exposure during pregnancy remains scarce and inconclusive. A previous study assessing the link between TMP-SMX exposure during pregnancy and the risk of spontaneous abortion (SA) did not control for protopathic bias and indication bias. METHODS: We conducted a nested control study (n = 77 429 pregnancies including 7039 cases of SA and 70 390 controls) within the Quebec Pregnancy Cohort. For each case of SA, we selected 10 controls at the index date that were matched on gestational age and year of pregnancy. TMP-SMX exposure was defined as either having filled at least one prescription between the first day of gestation (1DG) and the index date, or as having filled a prescription before pregnancy but with a duration overlapping the 1DG (102 pregnancies exposed to TMP-SMX, including 25 cases of SA and 77 controls). RESULTS: Adjusting for potential confounders, TMP-SMX exposure was associated with an increased risk of SA (AOR 2.94, 95% C 1.89-4.57, 25 exposed cases). Similar results were found after controlling for indication bias and protopathic bias. CONCLUSION: Given that this drug is widely use in HIV patients to prevent opportunistic infections and malaria, there is an urgent need to identify potential data sources in Africa for analysis of early pregnancy exposure to TMP-SMX.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".