Deprescribing tools: a review of the types of tools available to aid deprescribing in clinical practice
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
Abstract The importance of deprescribing, which is the process of withdrawing an inappropriate medication, supervised by a healthcare professional with the goal of managing polypharmacy and improving outcomes, is increasingly recognised as part of good clinical care. With this, a number of tools have been developed with the purpose of aiding health professionals to deprescribe in regular practice. The types of tools vary significantly in their form and include tools to aid in the overall process of deprescribing (such as generic frameworks and drug‐specific deprescribing guidelines) as well as tools that may assist in a specific part of the process (such as identifying inappropriate medications or engaging the patient). While many tools are available, most provide little (if any) information on how they were developed, and limited implementation research has been conducted. This paper provides an overview of the types of available tools and how they might be used in clinical practice.
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 | 0.023 | 0.167 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.006 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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