Counseling About Medication-Induced Birth Defects with Clinical Decision Support in Primary Care
Bibliographic record
Abstract
BACKGROUND: We evaluated how computerized clinical decision support (CDS) affects the counseling women receive when primary care physicians (PCPs) prescribe potential teratogens and how this counseling affects women's behavior. METHODS: Between October 2008 and April 2010, all women aged 18-50 years visiting one of three community-based family practice clinics or an academic general internal medicine clinic were invited to complete a survey 5-30 days after their clinic visit. Women who received prescriptions were asked if they were counseled about teratogenic risks or contraception and if they used contraception at last intercourse. RESULTS: Eight hundred one women completed surveys; 27% received a prescription for a potential teratogen. With or without CDS, women prescribed potential teratogens were more likely than women prescribed safer medications to report counseling about teratogenic risks. However, even with CDS 43% of women prescribed potential teratogens reported no counseling. In multivariable models, women were more likely to report counseling if they saw a female PCP (odds ratio: 1.97; 95% confidence interval: 1.26-3.09). Women were least likely to report counseling if they received angiotensin-converting enzyme inhibitors or angiotensin receptor blockers. Women who were pregnant or trying to conceive were not more likely to report counseling. Nonetheless, women who received counseling about contraception or teratogenic risks were more likely to use contraception after being prescribed potential teratogens than women who received no counseling. CONCLUSIONS: Physician counseling can reduce risk of medication-induced birth defects. However, efforts are needed to ensure that PCPs consistently inform women of teratogenic risks and provide access to highly effective contraception.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".