Drug-Drug Interaction Assessment and Identification in the Primary Care Setting
Bibliographic record
Abstract
BACKGROUND: Drug-drug interactions (DDIs) are ubiquitous, harmful and a leading cause of morbidity and mortality. With an aging population, growth in polypharmacy, widespread use of supplements, and the rising opioid abuse epidemic, primary care physicians (PCPs) are increasingly challenged with identifying and preventing DDIs. We set out to evaluate current clinical practices related to identifying and treating DDIs and to determine if opportunities to increase prevention of DDIs and their adverse events could be identified. METHODS: In a nationally representative sample of 330 board-certified family and internal medicine practitioners, we evaluated whether PCPs assessed DDIs in the care they provided for three simulated patients. The patients were taking common prescription medications (e.g. opioids and psychiatric medications) along with other common ingestants (e.g. supplements and food) and presented with symptoms of DDIs. Physicians were scored on their ability to inquire about the patient's medications, investigate possible DDIs, evaluate the patient, and provide treatment recommendations. We scored the physicians' care recommendations against evidence-based criteria, including overall care quality and treatment for DDIs. RESULTS: Average overall quality of care score was 50.5% ± 12.0%. Despite >99% self-reported use of medication reconciliation practices and tools, physicians identified DDIs in only 15.3% of patients, with 15.5% ± 20.3% of DDI-specific treatment by the physicians. CONCLUSIONS: PCPs in this study did not recognize or adequately treat DDIs. Better methods are needed to screen for DDIs in the primary care setting.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".