Bibliographic record
Abstract
Although most drugs are used to treat chronic or pregnancy-induced conditions during pregnancy and lactation, very few are studied in pregnant or breastfeeding women. The information we have on drugs taken during pregnancy and lactation is usually obtained after market approval through published case reports or case series and from pregnancy exposure or retrospective birth defect registries. Furthermore, generic drugs approved for use in this vulnerable population may be approved based on results from a male trial population. This disregards the changes that can occur during pregnancy which can affect the pharmacokinetics of drugs. In an effort to improve the information provided to prescribers, in 2008 the United States Food and Drug Administration proposed a change in product labelling where information from pregnancy exposure registries would be required. As of 2009, European Medicines Agency requires additional statements on use during pregnancy within drug labelling information. In Canada, it is anticipated that the efficacy and safety of drugs in pregnancy will be included under the Drug Safety and Effectiveness Network initiative, and that this will offer a unified approach for such assessments. Pregmedic, a non-profit organization for the advancement of safe and effective use of drugs in pregnancy, has presented a number of proposals and draft guidelines to Health Canada on the inclusion of pregnant women in pharmacokinetic studies and the establishment of registries for women who take drugs during pregnancy. Pregmedic advocates for ensuring that drugs indicated for women are studied in women.
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 | 0.015 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".