Potential Drug—Disease Interactions in Frail, Hospitalized Elderly Veterans
Bibliographic record
Abstract
BACKGROUND: Drugs can improve quality of life for many older people, but they may cause adverse health outcomes (eg, drug-disease interactions) if used inappropriately. OBJECTIVE: To determine the prevalence of potential drug-disease interactions as defined by explicit criteria and examine associations between sociodemographic and health status variables and potential drug-disease interactions. METHODS: The study design was cross-sectional. We evaluated 397 frail elderly inpatients from the Geriatric Evaluation and Management trial conducted at 11 Veterans Affairs Medical Centers. Drug-disease interactions were defined using explicit criteria from consensus expert panels of geriatricians from the US and Canada. RESULTS: Overall, 159 (40.1%) patients had one or more potential drug-disease interaction. The most common potential interactions were calcium-channel blockers and heart failure (12.3%) and beta-blockers and diabetes (6.8%). Multivariable logistic regression analyses revealed that age > or =75 years (adjusted OR 2.43; 95% CI 1.52 to 3.88), being married (adjusted OR 1.77; 95% CI 1.11 to 2.82), comorbidity index defined by Charlson method (adjusted OR 1.19; 95% CI 1.05 to 1.34), and use of multiple prescription drugs (5-8: adjusted OR 4.17; 95% CI 1.96 to 8.88, > or =9: adjusted OR 9.22; 95% CI 4.26 to 19.95), were significantly (p < 0.05) associated with having one or more potential drug-disease interaction. CONCLUSIONS: Potential drug-disease interactions are common in hospitalized elderly patients and are related to specific sociodemographic and health status factors. Further research is needed to examine the relationship between health outcomes and drug-disease interactions.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".