Patterns of Antidepressant Medication Use Among Pregnant Women in a United States Population
Bibliographic record
Abstract
This article describes the pattern of reported antidepressant use around the time of pregnancy in a population-based sample of women who delivered live-born babies without birth defects. Data were used from the National Birth Defects Prevention Study, an ongoing case-control study of risk factors for birth defects covering 10 US states. Mothers of live-born infants without birth defects (controls) born between 1998 and 2005 were randomly selected from each site. Information on the mother's characteristics and exposure to antidepressants was collected via a standardized telephone interview. Among 6582 mothers included in the study, 298 (4.5%) reported use of an antidepressant in the period of 3 months before through the end of pregnancy. Use of selective serotonin-reuptake inhibitors was reported most often (3.8%), followed by bupropion (0.7%). A statistically significant decline was observed, from 3.1% to 2.3% (P < .001), in reported use of antidepressants between the first and second month after conception. The frequency of reported antidepressant use at any time during pregnancy increased from 2.5% in 1998 to 8.1% in 2005 (P < .001) in 4 states. The findings show an increase in reported antidepressant use over a 9-year period and a substantial decrease in use around the usual time of pregnancy recognition.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".