NSAIDs and spontaneous abortions – true effect or an indication bias?
Bibliographic record
Abstract
AIM: The aim of the study was to characterize the extent of indication bias resulting from the excessive use of NSAIDs on the days preceding a spontaneous abortion to relieve pain. METHODS: We used data from a retrospective cohort study assessing the risk for spontaneous abortions following exposure to NSAIDs. Three definitions of exposure for cases of spontaneous abortions were compared, from the first day of pregnancy until the day of spontaneous abortion and until 3 and 2 days before a spontaneous abortion. Statistical analysis was performed using multivariate time programmed Cox regression. RESULTS: A sharp increase was observed in the dispensation of indomethacin, diclofenac and naproxen, and a milder increase was found in the use of ibuprofen during the week before a spontaneous abortion. Non- selective COX inhibitors in general and specifically diclofenac and indomethacin were found to be associated with spontaneous abortions when the exposure period was defined until the day of spontaneous abortion (hazard ratio (HR) 1.15, 95% confidence interval (CI) 1.04, 1.28; HR 1.31, 95% CI 1.08, 1.59 and HR 3.33, 95% CI 2.09, 5.29, respectively). The effect disappears by excluding exposures occurring on the day before the spontaneous abortion for non-selective COX inhibitors and on the last week before the spontaneous abortion for indomethacin. In general, decreasing HRs were found with the exclusion of exposures occurring on the days immediately before the spontaneous abortion. CONCLUSIONS: The increased use of NSAIDs during the last few days that preceded a spontaneous abortion to relieve pain associated with the miscarriage could bias studies assessing the association between exposure to NSAIDs and spontaneous abortions.
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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 | 0.110 | 0.237 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".