Ten deprescribing articles you should know about: A guide for newcomers to the field
Bibliographic record
Abstract
Authors on this article have authored and collaborated with authors of several of the studies mentioned in this commentary. Dr. Reeve is supported by an NHMRC Investigator Grant (GNT1195460) and grants from the National Institute on Aging (R33-AG057289 and R01-AG070047-01). She also receives royalties for co-authoring a chapter on deprescribing in UpToDate and honorarium from the Society of Hospital Pharmacists of Australia (leading workshops on deprescribing). Sion Scott has/has had research collaborations with INVISIO Pharmaceuticals, Desitin Pharma and iEthico but has no financial interest in the organizations. Michael A. Steinman is supported by grants from the US National Institute on Aging (R24AG064025, K24AG049057, P30AG044281) and receives honoraria from the American Geriatrics Society and from UpToDate for chapter authorship. Dr. McDonald is the creator and owner of the software MedSafer, licenced for used by MedSafer Corp. MedSafer provides electronic decision support for deprescribing. Dr. Farrell has received honoraria from the American College of Clinical Pharmacy for a textbook chapter and from the United States Deprescribing Research Network as a Scientific Advisory Board Member. Dr. Thompson has received a grant from the US Deprescribing Network (US National Institute on Aging).
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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 | 0.028 | 0.122 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.018 | 0.029 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.016 | 0.016 |
| Insufficient payload (model declined to judge) | 0.158 | 0.163 |
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".