Methodology for Developing Deprescribing Guidelines: Using Evidence and GRADE to Guide Recommendations for Deprescribing
Bibliographic record
Abstract
BACKGROUND: Class specific deprescribing guidelines could help clinicians taper and stop medications no longer needed or which may be causing more harm than benefit. We set out to develop methodology to create such guidelines using evidence-based methods for guideline development, evidence synthesis and recommendation rating. METHODS AND FINDINGS: Using a comprehensive checklist for a successful guideline enterprise, we conducted a national modified Delphi consensus process to identify priorities for deprescribing guidelines, then conducted scoping exercises to identify feasible topics, and sequentially developed three deprescribing guidelines. We selected guideline development team members for clinical expertise; a GRADE member worked with staff to ensure guideline development processes were followed. We conducted or used systematic searches and reviews of deprescribing trials of selected drug classes, reviews or systematic reviews of drug class effectiveness, reviews of reviews of drug class harm and narrative syntheses of contextual questions to inform recommendations and guideline development. Our 8 step process for guideline development included defining scope and purpose, developing a logic model to guide the process and generate key clinical questions, setting criteria for admissible evidence and conducting systematic reviews, synthesizing evidence considering additional contextual information and performing quality estimates, formulating recommendations and providing strength estimations, adding clinical considerations, conducting clinical and stakeholder review and finally updating content pre-publication. Innovative aspects of the guideline development process included synthesizing evidence for outcomes of tapering or stopping medication, and incorporating evidence for medication harm into the recommendation strength rating. Through the development of three deprescribing guidelines (for proton pump inhibitors, benzodiazepine receptor agonists and antipsychotics) and associated decision-support algorithms, we were able to gradually hone the methodology; each guideline will be published separately. CONCLUSION: Our methodology demonstrates the importance of searching for short and long-term outcomes, showing the benefits of deprescribing and studying patient preferences. This publication will support development of future deprescribing guidelines.
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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 | 0.415 | 0.651 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.007 | 0.014 |
| Bibliometrics | 0.047 | 0.027 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.014 | 0.013 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.012 | 0.006 |
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".