Pharmacotherapy and Pregnancy
Bibliographic record
Abstract
Data are sparse on the effects of medication use during pregnancy. \n \nHalf of the world's population is women. The majority of women become pregnant, and many of those women take some kind of medication during their pregnancy, even if only for a short time. The majority of drugs have not been rigorously studied in pregnant women to determine the most effective dose with the least potential for adverse effects. Instead, women are given “cookie‐cutter” therapy, using doses extrapolated from nonpregnant women, men, or pregnant animals. This can lead to problems. \n \nInstead, individualization of pharmacotherapy in pregnancy promises to take individual women and determine the optimal dose and drug for them to maximize the effect of the drug while attempting to minimize the side effects to them and their unborn babies. Because this field of study is underrepresented, we held a conference to bring together researchers and experts to discuss current knowledge, issues, and challenges surrounding individualized pharmacotherapy in pregnancy. Speakers came from the NIH, the Food and Drug Administration (FDA), and various research centers in the United States and Canada. Below are the summaries of the discussions at the conference. Full notes from the panel discussions are available from the authors on request.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".