Antidepressant Use around Conception, Prepregnancy Depression, and Risk of Ectopic Pregnancy
Bibliographic record
Abstract
OBJECTIVE: To compare the risk of ectopic pregnancy among women with and women without antidepressant prescriptions around conception and examine whether this risk differs by prepregnancy depression status. METHODS: We conducted a cohort study of all pregnancies between November 1, 2008, and September 30, 2015, identified in the nationwide (American) IBM® MarketScan® Databases. At least one day's supply of antidepressants in the 3 weeks after a woman's last menstrual period defined active antidepressant use around conception. At least one depression diagnosis in the year before the last menstrual period defined prepregnancy depression. Relative risk (RR) of ectopic pregnancy was estimated using unadjusted and inverse probability of treatment (IPT)-weighted log-binomial models. RESULTS: Of the 1,703,245 pregnancies, 106,788 (6.3%) women had a prepregnancy depression diagnosis. Among women with a depression diagnosis, 40,287 (37.7%) had an active antidepressant prescription around conception; the IPT-weighted risk of ectopic pregnancy was similar among women who did and did not fill an antidepressant prescription around conception (IPT-weighted RR = 1.01; 95% CI, 0.93 to 1.10). Overall, the risk of ectopic pregnancy was higher among women who had a prepregnancy depression diagnosis than women who did not have a prepregnancy depression diagnosis (IPT-weighted RR = 1.09; 95% CI, 1.04 to 1.15). CONCLUSIONS: This study's findings suggest that women who have a prepregnancy depression diagnosis are at a slightly increased risk of ectopic pregnancy, and among women who have a prepregnancy depression diagnosis, the use of antidepressants around conception does not increase the risk of ectopic pregnancy.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".