Incidence and Contributors to Potential Drug‐Drug Interactions in Hospitalized Patients
Bibliographic record
Abstract
Drug-drug interactions (DDIs) are drug combinations that result in pharmacological or clinical responses that differ from solitary administration. Previous studies of DDIs have been limited to particular drugs or particular patient populations. The authors performed a retrospective cohort study of all adults admitted to a teaching hospital between 1999 and 2005. All medications administered to patients were identified and compared with a standard reference of important DDIs. The authors measured the potential DDI incidence density as the percentage of time in the hospital during which patients were exposed to at least 1 DDI and used multivariate Poisson regression to determine its determinants. A total of 19.3% of 140 349 hospitalizations had at least 1 potential DDI. The potential DDI incidence density was 18.8%. Factors having the greatest influence on potential DDI incidence density included increased patient age (adjusted rate ratio patient >75 years vs <30 years, 2.25; 95% CI, 2.15-2.35), increased number of drug orders (adjusted rate ratio, 2.27 [2.23-2.30] for logarithm), and patient service (adjusted rate ratio, 1.49 [1.46-1.52] for surgical vs medical service). Potential DDIs were present during one fifth of hospitalization time.
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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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".