Measuring overuse: a deceptively complicated endeavour
Bibliographic record
Abstract
Choosing Wisely is a campaign aimed at reducing unnecessary tests, procedures and treatments.1 The goals of the campaign have been to both reduce healthcare expenditures and to prevent harms associated with inappropriate care—such as adverse effects of medications or radiation exposure from unwarranted imaging. The premise is clear and rather simple—do not order something the patient does not need. Yet, when applying a rigorous scientific improvement lens to reducing overuse, measurement nuances make evaluating this phenomenon anything but simple. Appropriateness is specific to clinical scenarios rather than being a representative property intrinsically coupled with individual tests or procedures. Assigning a label of ‘unnecessary’ to a test requires defining the target denominator, which can include a defined patient population, indication and/or test (ie, it is unnecessary to perform imaging for patients with low-back pain without risk factors). When quantifying appropriate use of a test or procedure, differences in the characterisation of the denominator will naturally affect the findings; this makes comparisons challenging when there is a lack of standardisation across measurement methodologies. In this issue of BMJ Quality & Safety , Muskens and colleagues present a systematic review of studies published up to February 2020 reporting the prevalence of low-value diagnostic testing.2 Studies were conducted in both ambulatory care and hospital settings; the findings yield prevalence estimates for the overuse of tests according to relevant guidelines such as Choosing Wisely, ‘Do not do recommendations’, the English National Institute for Health and Care Excellence and those from professional societies. As part of their analysis, Muskens and colleagues classify studies based on the category used for the denominator (referred to as ‘lenses’). Two categories or lenses are identified: (1) the service-centric lens that calculates the proportion of test indications defined as low value and (2) the patient-centric lens that determines the …
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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 | 0.301 | 0.570 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.006 | 0.047 |
| Scholarly communication | 0.021 | 0.049 |
| Open science | 0.008 | 0.013 |
| Research integrity | 0.017 | 0.038 |
| Insufficient payload (model declined to judge) | 0.011 | 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".