Categorization of deprescribing communication tools: A scoping review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Deprescribing can be beneficial to a wide variety of patients but is often not done due to barriers including lack of time and challenges starting conversations. OBJECTIVES: This study aimed to identify and broadly categorize existing deprescribing communication tools for clinicians and patients. METHODS: Our scoping review protocol was based on the Arksey and O'Malley methods and incorporated the Levac and Joanna Briggs Institute recommendations. EMBASE, CINAHL, PsycINFO, MEDLINE, and grey literature were searched, with two independent reviewers assessing eligibility. A backwards search of the texts chosen for full text screen was completed. Two reviewers independently completed data extraction using a pre-specified data collection form. FINDINGS: Databases identified 1121 results, searching of grey literature identified 49 results, and backwards searching identified 1323 results. After screening, 32 resources were included which contained 40 unique tools. Most tools were Canadian and targeted adults over 65 years old living in the community. Most tools had not been tested in the intended patient audience or evaluated for effectiveness. DISCUSSION: Deprescribing tools have been developed to facilitate conversations by providing structure, education, and decision-making approaches. More research is needed to test the effectiveness of existing tools.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it