What Are Priorities for Deprescribing for Elderly Patients? Capturing the Voice of Practitioners: A Modified Delphi Process
Bibliographic record
Abstract
Polypharmacy and inappropriate medication use among older adults contribute to adverse drug reactions, falls, cognitive impairment, noncompliance, hospitalization and mortality. While deprescribing - tapering, reducing or stopping a medication - is feasible and relatively safe, clinicians find it difficult to carry out. Deprescribing guidelines would facilitate this process. The aim of this paper is to identify and prioritize medication classes where evidence-based deprescribing guidelines would be of benefit to clinicians. A modified Delphi approach included a literature review to identify potentially inappropriate medications for the elderly, an expert panel to develop survey content and three survey rounds to seek consensus on priorities. Panel participants included three pharmacists, two family physicians and one social scientist. Sixty-five Canadian geriatrics experts (36 pharmacists, 19 physicians and 10 nurse practitioners) participated in the survey. Twenty-nine drugs/drug classes were included in the first survey with 14 reaching the required (≥ 70%) level of consensus, and 2 new drug classes added from qualitative comments. Fifty-three participants completed round two, and 47 participants completed round three. The final five priorities were benzodiazepines, atypical antipsychotics, statins, tricyclic antidepressants, and proton pump inhibitors; nine other drug classes were also identified as being in need of evidence-based deprescribing guidelines. The Delphi consensus process identified five priority drug classes for which expert clinicians felt guidance is needed for deprescribing. The classes of drugs that emerged strongly from the rankings dealt with mental health, cardiovascular, gastroenterological, and neurological conditions. The results suggest that deprescribing and overtreatment occurs through the full spectrum of primary care, and that evidence-based deprescribing guidelines are a priority in the care of the elderly.
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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 | 0.192 | 0.140 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".