A register study of the impact of stopping third trimester selective serotonin reuptake inhibitor exposure on neonatal health
Bibliographic record
Abstract
OBJECTIVE: To determine whether risk for adverse neonatal outcomes are reduced by stopping SSRI use before the end of pregnancy. METHOD: Using population health data, maternal health and prenatal SSRI prescriptions were linked to neonatal birth records (N = 119,547) (1998-2001). Neonates SSRI-exposed in the last 14 days (L14) of gestation were compared with infants who had gestational exposure, but not during the last 14 days (NL14). Propensity score matching was used to control for potential confounders (total exposure, maternal health characteristics). RESULTS: Increased risk for neonatal respiratory distress was present where L14 exposure occurred compared with risk where exposure stopped before L14. However, controlling for potential maternal and neonatal confounders, differences disappeared. CONCLUSION: Controlling for maternal illness severity, reducing exposure to SSRI's at the end of pregnancy had no significant clinical effect on improving neonatal health. These findings raise the possibility that some adverse neonatal outcomes may not be an acute pharmacological condition such as toxicity or withdrawal.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".