‘Can I stop even one of these pills?’ The development of a tool to make deprescribing easier
Bibliographic record
Abstract
While everyone might recognise the problems inherent in polypharmacy in the elderly, especially due to its many potential harms, there seems to be little consensus on how to reduce the medication burden on our older patients, and few tools to help clinicians initiate deprescribing. The medical system often supports interventions and shared decision-making in the introduction of evidence-based treatments, yet the literature suggests that without a framework to support polypharmacy risk reduction activities, clinicians do not feel confident about initiating ‘deprescribing.’ The prospect of deprescribing is fraught with considerable emotional and psychological stress for both practitioners and their patients. Previous studies1–3 have shown that many patients believe they are taking too many medications, yet despite the belief, there are barriers to asking their clinician to reduce their number of prescriptions. Patients fear their clinician's response,3 fear relapsing,4 fear being denied the ability to resume medicating5 and fear abandonment by their healthcare provider.4 Conversely, clinicians admit that deprescribing comes with anxiety, including uncertainty over why drugs were prescribed in the first place,6 fear that deprescribing will add to workload pressures5 and reluctance to engage patients in discussing quality-of-life/life expectancy issues.4 Clinicians have some unease about their ability to manage patients' discontinuation of medications and fear conflict with other healthcare providers.7 Despite these barriers, one irrefutable aspect in any discussion of deprescribing is its potential for overwhelming benefit. It can be, in the words of a group of New Zealand researchers, an ‘antidote’ to polypharmacy's many harms and write that deprescribing is “associated with numerous health benefits including improvement in cognition, a reduction in falls, a decrease in fractures, better medication adherence, and improvement in quality of life”.8 Interestingly, and perhaps more problematically, no comprehensive guidelines to date have been designed specifically … Correspondence to Dr Alan Cassels, University of Victoria, 423 Stannard Avenue, Victoria, British Columbia, Canada V8S 3M6; cassels{at}uvic.ca
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".