Evidence‐based medicine: A cornerstone for clinical care but not for quality improvement
Bibliographic record
Abstract
Quality improvement (QI) as a clinical improvement science has been criticized for failing to deliver broad patient outcome improvement and for being a top-down regulatory and compliance construct. These critics have argued that the focus of QI should be on increasing adherence to clinical practice guidelines (CPGs) and, as a result, should be consolidated into research structures with the science of evidence-based medicine (EBM) at the helm. We argue that EBM often overestimates the role of knowledge as the root cause of quality problems and focuses almost exclusively on the effectiveness of care while often neglecting the domains of safety, efficiency, patient-centredness, and equity. Successfully addressing quality problems requires a much broader, systems-based view of health-care delivery. Although essential to clinical decision-making and practice, EBM cannot act as the cornerstone of health system improvement.
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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 | 0.039 | 0.122 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.025 |
| Scholarly communication | 0.011 | 0.023 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.075 | 0.104 |
| Insufficient payload (model declined to judge) | 0.008 | 0.008 |
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".