Medical practice variations: what the literature tells us (or does not) about what are warranted and unwarranted variations
Bibliographic record
Abstract
This paper examines the sources of practice variations and definitions of unwarranted variation, as derived from the literature. The literature suggests variables/factors related to patient health needs, doctor 'practice style' and environmental constraints/opportunities as sources of practice variations. However, this list is likely to be incomplete because of significant unexplained variation in each study. Furthermore, it is unclear which factors are sources of unwarranted variation because the reviewed studies do not clearly discriminate between those variations that are unwarranted and those that are not. It is also unclear if context plays a role in determining if and when a factor is unwarranted. The literature contains few frameworks of what constitutes unwarranted variation. Among those offered, more information is needed regarding the scientific basis for including the selected factors, and how to operationalize the framework provided a particular one is chosen. A clear and consistent framework for unwarranted variation, and a clear indication how each component factor could be measured and integrated can help investigators determine which variables should be included in their studies, such that the sources of unwarranted variations may be identified. A better understanding of the role of patient preference as a potential source of practice variations is also required.
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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 | 0.056 | 0.224 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.014 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".