Safety of fluoxetine during the first trimester of pregnancy: a meta-analytical review of epidemiological studies
Bibliographic record
Abstract
BACKGROUND: This study was designed to examine whether there is an increased risk for major malformations following the use of fluoxetine during the first trimester of pregnancy. METHODS: Published and unpublished reports were identified through computerized and manual searches of bibliographical databases, reference lists from primary articles, and letters to editors, agencies, foundations and content experts. Meta-analysis was undertaken of prospective controlled and uncontrolled studies on the use of fluoxetine during first trimester of pregnancy. RESULTS: The pooled relative risk and 95% confidence interval for major malformations does not suggest an association between the use of fluoxetine during the first trimester and an increased risk of major malformations. Combination of controlled and uncontrolled studies shows a weighted risk of 26% (95% CI 1-4.2%). The summary odds ratio from the two controlled studies (OR = 1.33, 95% CI 0.49-3.58) was not significant. Homogeneity testing shows that the effect sizes are similar throughout all studies. Power analysis indicates that 26 controlled studies of similar size, would be required, to reverse this finding. CONCLUSIONS: The use of fluoxetine during the first trimester of pregnancy is not associated with measurable teratogenic effects in human.
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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 | 0.014 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.016 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".