Deprescribing: moving from evidence to implementation
Bibliographic record
Abstract
Background: Polypharmacy (taking 5 or more medications) affects almost two-thirds of older Canadian adults.It is associated with adverse drug events (ADEs) such as falls, fractures, hospitalization, and death.Deprescribing, the clinically supervised process of stopping or reducing the dose of medications that are harmful, extraneous, or no longer beneficial, can reduce the negative effects of polypharmacy.However, some clinicians are still hesitant to deprescribe for fear of causing potential adverse drug withdrawal events (ADWEs), defined as the re-emergence of disease symptoms, or rebound effects once a medication is discontinued.Techniques to quantify and mitigate ADWEs represent an important step forward.Scaling interventions to reach as many patients as possible is also important.Our research team recently adapted MedSafer, an electronic deprescribing decision support tool developed for healthcare professionals, to allow patients and caregivers to initiate the deprescribing process on the consumer end.However, the adoption of MedSafer by patients and caregivers' hinges on its acceptability and usability by the end-user.
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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 | 0.406 | 0.594 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.031 | 0.037 |
| Open science | 0.014 | 0.024 |
| Research integrity | 0.025 | 0.032 |
| Insufficient payload (model declined to judge) | 0.022 | 0.004 |
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".