Effects of timing and duration of gestational exposure to serotonin reuptake inhibitor antidepressants: Population-based study
Bibliographic record
Abstract
BACKGROUND: Late-gestational serotonin reuptake inhibitor (SRI) exposure has been linked to adverse neonatal outcomes; however, the impact of timing and duration of exposure is unknown. AIMS: To determine whether late-gestational exposure to an SRI is associated with increased risk of adverse neonatal outcome relative to early exposure. METHOD: Population-based maternal and neonatal health records were linked to prenatal maternal prescription records for an SRI medication (n=3500). RESULTS: After controlling for maternal illness and duration of exposure, using propensity score matching, neonatal outcomes did not differ between late and early exposure (P>0.05). After controlling for maternal illness, longer prenatal exposure increased the risks of lower birth weight, respiratory distress and reduced gestational age (P<0.05). CONCLUSIONS: Using population health data, length of gestational SRI exposure, rather than timing, increased the risk for neonatal respiratory distress, lower birth weight and reduced gestational age, even when controlling for maternal illness and medication dose. These findings highlight the importance of distinguishing the specific impact of medication exposure from exposure to maternal illness itself.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".