Inappropriate prescribing in the elderly
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Drug therapy is necessary to treat acute illness, maintain current health and prevent further decline. However, optimizing drug therapy for older patients is challenging and sometimes, drug therapy can do more harm than good. Drug utilization review tools can highlight instances of potentially inappropriate prescribing to those involved in elderly pharmacotherapy, i.e. doctors, nurses and pharmacists. We aim to provide a review of the literature on potentially inappropriate prescribing in the elderly and also to review the explicit criteria that have been designed to detect potentially inappropriate prescribing in the elderly. METHODS: We performed an electronic search of the PUBMED database for articles published between 1991 and 2006 and a manual search through major journals for articles referenced in those located through PUBMED. Search terms were elderly, inappropriate prescribing, prescriptions, prevalence, Beers criteria, health outcomes and Europe. RESULTS AND DISCUSSION: Prescription of potentially inappropriate medications to older people is highly prevalent in the United States and Europe, ranging from 12% in community-dwelling elderly to 40% in nursing home residents. Inappropriate prescribing is associated with adverse drug events. Limited data exists on health outcomes from use of inappropriate medications. There are no prospective randomized controlled studies that test the tangible clinical benefit to patients of using drug utilization review tools. Existing drug utilization review tools have been designed on the basis of North American and Canadian drug formularies and may not be appropriate for use in European countries because of the differences in national drug formularies and prescribing attitudes. CONCLUSION: Given the high prevalence of inappropriate prescribing despite the widespread use of drug-utilization review tools, prospective randomized controlled trials are necessary to identify useful interventions. Drug utilization review tools should be designed on the basis of a country's national drug formulary and should be evidence based.
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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 | 0.006 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".