Bibliographic record
Abstract
Research in hospital variation is important and currently very popular. However, due to the methods employed in such studies—namely, the retrospective mining of large datasets and the use of several alternative variation groupings—some results may be spurious. In this commentary, we perform an empirical analysis of the 50 most highly cited and the 50 most recent papers focusing on variation in medical care. Across these studies, we identify at least 13 unique groupings and could find no single instance where a medical practice was found not to vary. We go on to discuss one example of variation—statin use—in more detail to elucidate the tensions that these studies often create. Together, these results suggest that multiple hypothesis testing is a concern for variation research. Finally, we outline strategies to mitigate this concern. RÉSUMÉ La recherche sur la variation hospitalière est importante et actuellement très populaire. Toutefois, en raison des méthodes employées dans de telles études—notamment, l’extraction rétrospective de grands ensembles de données et l’utilisation de plusieurs groupe- ments de variation alternatifs—certains résultats peuvent être fautifs. Dans ce commentaire, nous effectuons une analyse empirique des 50 articles les plus citées et des 50 articles les plus récents se concentrant sur la variation dans les soins médicaux. Dans ces études, nous identifions au moins 13 groupements uniques, et ne pouvions trouver aucun cas où une pratique médicale ne variait pas. Nous discutons ensuite d’un exemple de variation—dans l’utilisation de statines—en plus de détails afin d’élucider les tensions que ces études suscitent souvent. Collectivement, ces résultats suggèrent que la mise à l’essai de multiples hypothèses est une préoccupation lors de la recherche sur la variation. Finalement, nous décrivons des stratégies pour atténuer cette préoccupation.
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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 | 0.311 | 0.605 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.010 |
| Bibliometrics | 0.009 | 0.015 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 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; 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".