Bibliographic record
Abstract
In response to concerns about increasing utilization of lowvalue health care services, the American Board of Internal Medicine Foundation launched the Choosing Wisely cam paign in the United States in 2012.1 The goal of the campaign is to encourage conversations between physicians and patients about low value care by defining “top 5 ” lists of tests, treatments and procedures that may be unneces sary or unsupported by evidence.1 Subsequent Choosing Wisely campaigns have followed in other countries, including Canada starting in April 2014.2,3 Of interest for health policy makers, payers and clinicians are current utiliza tion rates for the procedures mentioned in these recommendations. Establishing baseline rates permits an understanding of the extent of the problem of lowvalue care, which in turn allows monitoring of the effect of initiatives such as Choosing Wisely on utilization rates over time. One Choosing Wisely item included by many specialty societies is the recommendation to avoid routinely performing preoperative testing (including chest radiography, echocardiography and cardiac stress tests) for patients undergoing lowrisk surgery.4–6 This recommendation was previously included in the 2007 American Col lege of Cardiology/American Heart Association guidelines on perioperative cardiovascular evalu ation for noncardiac surgery7 and was recon firmed in a recent update.8 Avoiding preopera tive investigations in this setting is supported by Preoperative testing before low-risk surgical procedures
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.898 | 0.708 |
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".