Choosing Wisely – An international and multimorbid perspective
Bibliographic record
Abstract
Some medical diagnostic and therapeutic interventions are non-beneficial or even harmful. The Choosing Wisely campaign has encouraged the generation of "top five" lists of unnecessary low-value services in different specialist areas. In the USA alone, where the campaign was launched, these lists include a total of 450 evidence-based recommendations. Medical scientific societies in further countries such as Canada, Australia, New Zealand, England, Switzerland and Germany have since initiated Choosing Wisely campaigns. Besides implementing top five lists, these aim to change attitudes, expectations and practices in the culture of medicine. The field of internal medicine has initiated change in Switzerland (Swiss Society of General Internal Medicine: Smarter Medicine) and Germany (German Society of Internal Medicine: Klug entscheiden). Formulating Choosing Wisely principles in managing complex patients with multiple concurrent acute or chronic diseases, i. e., multimorbidity (MM), will present a particular challenge. Research is needed to determine the primary sources of overuse in specific combinations of diseases (i. e., MM clusters) and spearhead corresponding recommendations. National Choosing Widely campaigns may serve as a forerunner to a more global initiative.
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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 | 0.015 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.012 | 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".