Herbal products use during pregnancy: prevalence and predictors
Bibliographic record
Abstract
PURPOSES: (1) Measure the prevalence of herbal product (HP) use, alone, and concomitantly with prescribed medications during pregnancy, (2) identify the most frequently consumed HP during gestation and (3) determine predictors of HP use at the beginning of pregnancy, and during the third trimester. METHODS: A questionnaire was mailed to 8505 women selected from the Quebec Pregnancy Registry which was created by the linkage of three administrative databases: RAMQ, Méd-Echo and ISQ. Women were eligible if they were continuously insured by the RAMQ drug plan for at least 12 months before the first day of gestation and during pregnancy, and if they gave birth to a live born between January 1998 and December 2003 in one of the Quebec's hospitals. Women with diabetes and psychoses, and women who delivered a baby with birth defects were selected first. Descriptive statistics and multivariate logistic regression models were used to analyse data. RESULTS: Of the 3354 women (39%) who answered the questionnaire, and were included in the study, nine per cent used HP during pregnancy. 69% of users took at least one prescribed medication concomitantly. Chamomile, green tea, peppermint and flax were the most frequently HP used. Multivariate analyses showed that body mass index (BMI), multivitamin use and one to three prescribed medications used before pregnancy were predictors of HP use at the beginning of pregnancy; adherent women, smokers and users of HP prior to pregnancy were predictors of HP use during the third trimester. CONCLUSION: HP use alone and concomitantly with prescribed medications during pregnancy is common, and needs to be addressed by health professionals.
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 | 0.001 |
| 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".