Cochrane Sustainable Healthcare: evidence for action on too much medicine
Bibliographic record
Abstract
Medical excess threatens the health of individuals and the sustainability of health systems.[1][2][3] Unnecessary tests, treatments, and diagnoses bring direct harm to people through adverse effects of interventions,[3][4] psychosocial impacts of labelling,[5] and overwhelming burden of treatment.[6]Overuse and overdiagnosis also consumes scarce resources, leading to underuse and underdiagnosis in other areas, which indirectly harms patients.[7] As healthcare spending grows all over the world, [8] with poor correlation between increased costs and improved health in high-income countries,[9] there is growing recognition that much of that spending is unnecessary.[1][2] Increased costs of healthcare also draws resources from other societal sectors capable of improving health and wellbeing for the population.[10] By tackling the crisis of medical excess, we can reduce harm and prevent waste, making our health systems more sustainable and more beneficial for patients and societies.
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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 | 0.029 | 0.165 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.006 |
| Bibliometrics | 0.016 | 0.012 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.015 | 0.015 |
| Insufficient payload (model declined to judge) | 0.022 | 0.006 |
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".