A comprehensive systematic review of the types of medication classes targeted by deprescribing tools and the tools/interventions applied to each class
Bibliographic record
Abstract
INTRODUCTION: Polypharmacy and potentially inappropriate medications (PIMs) contribute to adverse outcomes. Deprescribing, the supervised withdrawal of PIMs, is a key strategy to reduce these risks. Identifying the most targeted PIMs and commonly used tools for deprescribing remains essential. The aim of this systematic review was to identify the medication classes targeted by deprescribing tools, stratified by tool type and frequency and to evaluate the availability of medication-specific tools. METHODS: A systematic search of Embase, PubMed, Scopus, and Medline (2010-2023) identified observational and experimental studies using polypharmacy and deprescribing terms. The Newcastle-Ottawa Scale (NOS) and the revised Cochrane risk-of-bias tool (RoB 2) were used for quality assessment. Medication classes and tools/interventions were summarized in the TOTALLY TARGETED List. RESULTS: Eighty-two studies identified 44 deprescribing tools targeting 77 medication classes. The top PIMs were benzodiazepines, antipsychotics, alpha-receptor blockers, proton pump inhibitors, and Z-hypnotics. The most used tools were Screening Tool of Older People's Prescriptions (STOPP) Frail, American Geriatric Society (AGS) Beers Criteria, STOPP Criteria, and STOPPFall. Several medication-specific tools (e.g. PIMs in cognitively impaired patients) were also identified. CONCLUSION: This review identified the most targeted medication classes and deprescribing tools, emphasizing the need for medication-specific and patient-centered approaches to improve safety and outcomes. PROTOCOL REGISTRATION: https://www.crd.york.ac.uk/prospero//CRD42023442654.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.008 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".