Reducing potentially inappropriate polypharmacy at a national and international level: the impact of deprescribing networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
INTRODUCTION: Over the past decade, polypharmacy has increased dramatically. Measurable harms include falls, fractures, cognitive impairment, and death. The associated costs are massive and contribute substantially to low-value health care. Deprescribing is a promising solution, but there are barriers. Establishing a network to address polypharmacy can help overcome barriers by connecting individuals with an interest and expertise in deprescribing and can act as an important source of motivation and resources. AREAS COVERED: Over the past decade, several deprescribing networks were launched to help tackle polypharmacy, with evidence of individual and collective impact. A network approach has several advantages; it can spark interest, ideas and enthusiasm through information sharing, meetings and conversations with the public, providers, and other key stakeholders. In this special report, the details of how four deprescribing networks were established across the globe are detailed. EXPERT OPINION: Networks create links between people who lead existing and/or budding deprescribing practices and policy initiatives, can influence people with a shared passion for deprescribing, and facilitate sharing of intellectual capital and tools to take initiatives further and strengthen impact.This report should inspire others to establish their own deprescribing networks, a critical step in accelerating a global deprescribing movement.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it