What do clinical practice guidelines say about deprescribing? A scoping review
Bibliographic record
Abstract
Introduction Deprescribing (medication dose reduction or cessation) is an integral component of appropriate prescribing. The extent to which deprescribing recommendations are included in clinical practice guidelines is unclear. This scoping review aimed to identify guidelines that contain deprescribing recommendations, qualitatively explore the content and format of deprescribing recommendations and estimate the proportion of guidelines that contain deprescribing recommendations. Methods Bibliographic databases and Google were searched for guidelines published in English from January 2012 to November 2022. Guideline registries were searched from January 2017 to February 2023. Two reviewers independently screened records from databases and Google for guidelines containing one or more deprescribing recommendations. A 10% sample of the guideline registries was screened to identify eligible guidelines and estimate the proportion of guidelines containing a deprescribing recommendation. Guideline and recommendation characteristics were extracted and language features of deprescribing recommendations including content, form, complexity and readability were examined using a conventional content analysis and the SHeLL Health Literacy Editor tool. Results 80 guidelines containing 316 deprescribing recommendations were included. Deprescribing recommendations had substantial variability in their format and terminology. Most guidelines contained recommendations regarding forwho(75%, n=60), what(99%, n=89) andwhen or why(91%, n=73) to deprescribe, however, fewer guidelines (58%, n=46) contained detailed guidance onhowto deprescribe. Approximately 29% of guidelines identified from the registries sample (n=14/49) contained one or more deprescribing recommendations. Conclusions Deprescribing recommendations are increasingly being incorporated into guidelines, however, many guidelines do not contain clear and actionable recommendations onhowto deprescribe which may limit effective implementation in clinical practice. A co-designed template or best practice guide, containing information on aspects of deprescribing recommendations that are essential or preferred by end-users should be developed and employed. Trial registration number osf.io/fbex4.
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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 | 0.057 | 0.350 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.030 | 0.033 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".