The <i>Choosing Wisely</i> campaign to reduce harmful medical overuse: its close association with <i>Patient Blood Management</i> initiatives
Bibliographic record
Abstract
Eliminating unnecessary diagnostic procedure and treatments in medicine is a cornerstone of doing no harm, improving patient care and outcomes and allocating resources appropriately.A recent report by the Academy of Medical Royal Colleges argued that doctors have an ethical responsibility to reduce this wastage of clinical resource because, in a healthcare system with finite resources, one doctor's waste is another patient's delay (Academy of Royal Medical Colleges, 2014).It has always been difficult to engage clinicians in stopping familiar or ingrained practices and procedures as it requires a different approach to that for introducing new treatments.A new initiative developed in the United States and Canada called Choosing Wisely (www.choosingwisely.org)aims to change doctors' practice to align with best practice by getting them to stop using various interventions that are not supported by evidence, free from harm and truly necessary, including those that duplicate tests or procedures already received and it will soon be applied to transfusion medicine.
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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 | 0.020 | 0.081 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.006 | 0.015 |
| Insufficient payload (model declined to judge) | 0.020 | 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 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".