Efficacy of a clinical medication review on the number of potentially inappropriate prescriptions prescribed for community-dwelling elderly people.
Bibliographic record
Abstract
BACKGROUND: The administration of many drugs concurrently to elderly patients is a well-known problem in geriatrics and involves numerous risks. One way to reduce polypharmacy is to provide information to physicians in order to modify their prescribing practices. The main objective of this study was to evaluate the impact of an intervention program that targeted physicians with the aim of reducing the number of potentially inappropriate prescriptions (PIPs) given to elderly patients. METHODS: A randomized controlled trial was carried out among community-dwelling elderly people in Sherbrooke, Que. The participants were 266 patients over 75 years of age (experimental group: n = 136, control group: n = 130). A team comprising 2 physicians, a pharmacist and a nurse reviewed the list of drugs and the diagnoses of a subgroup of the experimental group in a case conference. Suggestions were formulated and mailed to the patients' physicians together with relevant scientific documentation justifying the recommendations. The main outcome measure was the number of PIPs. RESULTS: The mean number of PIPs per patient declined by 0.24 in the experimental group (n = 127) and by 0.15 in the control group (n = 116). The decline in PIPs was even larger in the experimental group that had case conferences (n = 80), in which the mean number of PIPs per patient declined by 0.31. However, this difference between the experimental group and the control group was not statistically significant in the intent-to-treat analysis. The number of drugs prescribed was not modified by the intervention, nor were the results of the global assessment of the patients' drug profiles. INTERPRETATION: This study suggests that the intervention program had no effect on the prescribing of PIPs.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".