Deprescribing: What is the gold standard? Themes that characterized the discussions at the first Danish symposium on evidence-based deprescribing
Bibliographic record
Abstract
The first Danish symposium on evidence-based deprescribing was held in September 2019. The symposium aimed to increase the awareness of deprescribing in general, to discuss the importance of deprescribing, and, thus, a potential consensus on key issues on a national deprescribing agenda. The invited keynote speaker, Barbara Farrell, from the Bruyére Research Institute, Ottawa, Canada, presented their thorough work on deprescribing guideline development and application. The symposium consisted of two parts: Part 1 concentrated on establishing the need for deprescribing in our society. Part 2 consisted of a panel debate that put the practical application and implementation of deprescribing in perspective to the input from the audience and the structure of the Danish healthcare system. The panelists represented key stakeholders, e.g., clinical pharmacists, general practitioners, hospital doctors, Danish Health Authority representatives, health politicians concerning deprescribing in Denmark. The event allowed 145 participants to discuss the importance of implementing deprescribing in a Danish setting. This commentary highlights and discusses the major themes that characterized the symposium: "why deprescribe?", "deprescribing research" and a theme dedicated to "problems of concern." The emergence of these themes formed the basis for the discussion of new strategies and a proposal for a future gold standard to succeed in deprescribing.
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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 | 0.166 | 0.150 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.017 | 0.045 |
| Scholarly communication | 0.022 | 0.020 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.027 | 0.052 |
| Insufficient payload (model declined to judge) | 0.002 | 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".