Failure to place evidence at the centre of quality improvement remains a major barrier for advances in quality improvement
Bibliographic record
Abstract
Mondoux and Shojania (M&S) issued a critique of our call to unify all disciplines of relevance for quality improvement (QI). They do not challenge the need for alignment of different fields that have played roles in the QI space. They selected to focus their critique on our views that ultimately the discipline of QI should be based on the principles of evidence-based medicine (EBM) and decision sciences. In our response, we reaffirm our calls to help achieve needed alignment and integration of all disciplines of importance to QI through "a unifying framework for improving health care" with EBM and decision sciences at helm. Challenging the importance of placing QI on solid empirical basis is misguided: As QI is all about measuring and consequently improving clinical care, acting on reliable evidence must remain its "cornerstone". Apparent differences in our views appears to be due to our focus on what care should be delivered, while M&S concentrate on how that care should be delivered. The former is the domain of a narrowly defined EBM, while the latter is the realm of improvement/implementation science-which, we argue, should also be evidence-based. QI initiatives are fundamentally local activities, and regulators would be most helpful if they require each institution to provide an annual plan of its top QI activities not included in the existing mandated list of performance measures. Finally, we addressed a number of specific QI initiatives highlighted by M&S-use of opioids, handwashing, venous-thromboembolism prophylaxis, hip replacement, and perioperative beta-blockers-to show that they would have been carried differently if they were based on the principles of EBM. Thus, the failure to place evidence at the centre remains a major barrier for advances in QI.
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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 | 0.432 | 0.595 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.007 | 0.040 |
| Scholarly communication | 0.028 | 0.036 |
| Open science | 0.010 | 0.023 |
| Research integrity | 0.020 | 0.067 |
| Insufficient payload (model declined to judge) | 0.008 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".