Medicine optimization strategy in an acute geriatric unit: The pharmacist in the geriatric team
Bibliographic record
Abstract
AIM: Older patients admitted to acute geriatric units (AGU) frequently use many medications and are particularly vulnerable to adverse drug events, so specific interventions in this setting are required. In the present study, we describe a new medicine optimization strategy in an AGU, and explore its potential in reducing polypharmacy and improving medication appropriateness. METHODS: The present prospective study included patients aged ≥75 years who were admitted to an AGU in a tertiary hospital. An intervention based on a pharmacist clinical interview, medication history and a structured medication review within a comprehensive geriatric assessment was proposed. The differences regarding polypharmacy as the primary outcome (≥5 chronic drugs), hyperpolypharmacy (≥10), number of drugs, drug-related problems and Screening Tool of Older Person's Prescription/Screening Tool to Alert Doctors to Right Treatment criteria between admission and discharge were evaluated. RESULTS: From October 2016 to April 2017, 234 patients were enrolled, aged 87.6 years (SD 4.6 years); 143 (61.1%) were women. The intervention resulted in a statistically significant improvement in polypharmacy (-10.2%, 95% CI -15.3, -5.2), hyperpolypharmacy (-16.6%, 95% CI -22.3 -11.0), number of medications (-1.4, 95% CI -1.8, -1.0), Screening Tool of Older Person's Prescription criteria (-19.2%, 95% CI -24.9, -13.6), Screening Tool to Alert Doctors to Right Treatment criteria (-6.8%, 95% CI -10.1, -3.5) and drug-related problems (-2.7, 95% CI -2.9, -2.4; P ≤ 0.001 for all). CONCLUSIONS: A systematic pharmacist-led intervention at hospital admission to an AGU within a comprehensive geriatric assessment was associated to a decrease in polypharmacy, drug-related problems and potentially inappropriate prescribing. Geriatr Gerontol Int 2019; 19: 530-536.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".