Drug-drug interactions and their predictors: results from Indian elderly inpatients
Bibliographic record
Abstract
BACKGROUND: In view of the multiple co-morbidities, the elderly patients receiving drugs are prone to suffer with drug interactions since they receive a greater number of drugs. OBJECTIVE: The study was undertaken to determine the prevalence of drug interactions, as well as their predictors. METHODS: The prescriptions of a total of 1510 inpatients were collected prospectively for 1.5 years from inpatients wards of public tertiary care teaching hospital. All the prescriptions were checked for drug interactions using the Micromedex® Drug-Reax database-2010 and Stockley's Drug Interactions. Regression analyses sought to determine predictors for the drug interaction. RESULTS: The patients, with the average age of 67.2 ±0.2 years, were prescribed an average of 9.15 ±0.03 medications. It was found that out of 1510 prescriptions of inpatients, 126 (8.3%) prescriptions had one or more than one drug interaction. All the identified interactions were severe in nature. The top most interacting drugs were acetylsalicylic acid and anticoagulant (n=59). The second top most interacting drug combination was clopidogrel and proton pump inhibitors (n=51). The most commonly involved drugs in interactions were C (cardiovascular system) and A (alimentary tract and metabolism). Using multivariate binary logistic regression, multiple drugs (Odds Ratio=4.5; 95% Confidence Interval: - 2.38 -9.47) and multiple diagnoses (Odds Ratio=2.6; 95%CI: -1.40 -5.57) were found to be significant predictors for drug interaction. CONCLUSIONS: The results of this study substantiate the occurrence of severe drug interactions among Indian elderly inpatients. In order to provide safer pharmaceutical care, the active involvement of clinical pharmacists is a potential option.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".