Choosing Wisely in a time of resource constraints
Bibliographic record
Abstract
Healthcare systems globally are facing multiple intersecting and overlapping crises including unprecedented resource constraints and a burnt out, demoralised workforce.1 Clinicians are being asked to do more with less in the face of backlogs in access to services alongside rising health inequalities and increases in patient complexity.Evidence based ways to curb wasteful spending and encourage sustainability are important as healthcare systems grapple with ongoing crises, brace for new eventualities, and aim to become more resilient.There is increased urgency, now more than ever before, to avoid waste in healthcare by eliminating overuse in healthcare.Choosing Wisely campaigns, now in over 30 countries globally, offer an approach to reducing overuse and waste based on clinician developed evidence based recommendations.
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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 | 0.017 | 0.076 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.024 | 0.042 |
| Insufficient payload (model declined to judge) | 0.022 | 0.019 |
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".