The incidence of nausea and vomiting of pregnancy (NVP): a comparison between depressed women treated with antidepressants and non-depressed women.
Bibliographic record
Abstract
BACKGROUND: Nausea and vomiting of pregnancy (NVP) affects up to 80% of pregnant women. In many cases NVP causes changes i n family, social, o roccupational functioning. Several studies have linked NVP with depression; however, whether depression preceded or resulted from NVP, has not been established. OBJECTIVE: To examine whether pregnant women, diagnosed with depression pre-conceptionally, treated with an antidepressant, reported a higher incidence of NVP when compared with pregnant women without depression. METHOD: In this pilot study, two groups of pregnant women who called the Motherisk Program in Toronto, Canada, were compared. Group 1 was comprised of 179 pregnant women who reported taking an antidepressant for the treatment of depression prior to pregnancy and in the first trimester. Group 2 was comprised of 179 pregnant women with no history of depression. The incidence of NVP in both groupswas recorded and compared. RESULTS: In the depressed group 109/179 (61%) women reported suffering from NVP vs.121/179 (68%) in the non-depressed group (P = 0.1). The logistic regression analysis did not identify any independent variable as significantly explaining NVP. CONCLUSION: Depression and treatment with antidepressants prior to and in early pregnancy, does not appear to affect the incidence of NVP.
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) | 0.001 | 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".