Strategies and Tools for Supporting the Appropriateness of Drug Use in Older People
Bibliographic record
Abstract
Through this structured review of the published literature, we aimed to provide an up-to-date description of strategies (human-related) and tools (mainly from the digital field) facilitating the appropriateness of drug use in older adults. The evidence of each strategy and tool's effectiveness and sustainability largely derives from local and heterogeneous experiences, with contrasting results. As a general framework, three main steps should be considered in implementing measures to improve appropriateness: prescription, acceptance by the patient, and continuous monitoring of adherence and risk-benefit profile. Each step needs efforts from specific actors (physicians, patients, caregivers, healthcare professionals) and dedicated supporting tools. Moreover, how to support the appropriateness also strictly depends on the particular setting of care (hospital, ambulatory or primary care, nursing home, long-term care) and available economic resources. Therefore, it is urgent assigning to each approach proposed in the literature the following characteristics: level of effectiveness, strength of evidence, setting of implementation, needed resources, and issues for its sustainability.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".