Safe lists for medications in pregnancy: inadequate evidence base and inconsistent guidance from Web‐based information, 2011
Bibliographic record
Abstract
PURPOSE: Medication use during pregnancy is common and increasing. Women are also increasingly getting healthcare information from sources other than their physicians. METHODS: This report summarizes an environmental scan that identified 25 active Internet sites that list medications reported to be safe for use in pregnancy and highlights the inadequate evidence base and inconsistent guidance provided by these sites. RESULTS: These lists included 245 different products, of which 103 unique components had been previously evaluated in terms of fetal risk by the Teratogen Information System (TERIS), a resource that assesses risk of birth defects after exposure under usual conditions by consensus of clinical teratology experts. For 43 (42%) of the 103 components that were listed as 'safe' on one or more of the Internet sites surveyed, the TERIS experts were unable to determine the fetal risk based on published scientific literature. For 40 (93%) of these 43, either no data were available to assess human fetal risk or the available data were limited. CONCLUSIONS: Women who see a medication on one of these 'safe' lists would be led to believe that there is no increased risk of birth defects resulting from exposure. Thus, women are being reassured that fetal exposure to these medications is safe even though a sufficient evidence base to determine the relative safety or risk does not exist.
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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 | 0.067 | 0.317 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| 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".