Potentially inappropriate prescriptions for older patients in long-term care
Bibliographic record
Abstract
BACKGROUND: Inappropriate medication use is a major healthcare issue for the elderly population. This study explored the prevalence of potentially inappropriate prescriptions (PIPs) in long-term care in metropolitan Quebec. METHODS: A cross sectional chart review of 2,633 long-term care older patients of the Quebec City area was performed. An explicit criteria list for PIPs was developed based on the literature and validated by a modified Delphi method. Medication orders were reviewed to describe prescribing patterns and to determine the prevalence of PIPs. A multivariate analysis was performed to identify predictors of PIPs. RESULTS: Almost all residents (94.0%) were receiving one or more prescribed medication; on average patients had 4.8 prescribed medications. A majority (54.7%) of treated patients had a potentially inappropriate prescription (PIP). Most common PIPs were drug interactions (33.9% of treated patients), followed by potentially inappropriate duration (23.6%), potentially inappropriate medication (14.7%) and potentially inappropriate dosage (9.6%). PIPs were most frequent for medications of the central nervous system (10.8% of prescribed medication). The likelihood of PIP increased significantly as the number of drugs prescribed increased (odds ratio [OR]: 1.38, 95% confidence interval [CI]: 1.33-1.43) and with the length of stay (OR: 1.78, CI: 1.43-2.20). On the other hand, the risk of receiving a PIP decreased with age. CONCLUSION: Potentially inappropriate prescribing is a serious problem in the highly medicated long-term care population in metropolitan Quebec. Use of explicit criteria lists may help identify the most critical issues and prioritize interventions to improve quality of care and patient safety.
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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 | 0.004 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".