Low value cardiac testing and Choosing Wisely
Bibliographic record
Abstract
Just a few years prior to the famous Institute of Medicine (IOM) reports on safety and quality, an earlier IOM report classified all quality problems in healthcare as falling into three broad categories: underuse, overuse and misuse.1 Until recently, however, the focus on quality has been almost exclusively on the underuse and misuse, and overuse has received much less attention.2 This focus is particularly surprising, as much of the early work in healthcare quality focused on overuse. In particular, early research done on the geographical variation of healthcare service delivery implied that a significant proportion of healthcare services, such as surgical procedures, were not necessary.3 ,4 Despite this early research, the majority of quality improvement efforts over the past decade were directed towards improving patient safety and addressing care gaps related to the underuse of health services. More recently, with healthcare systems worldwide struggling to contain rising costs, overuse of healthcare services is beginning to receive more attention. In an effort at bringing attention to the generally accepted notion that excessive use of low-value care is a contributor to those costs, and that overuse of tests and treatments may lead to potential patient harm, the American Board of Internal Medicine Foundation launched the Choosing Wisely campaign in 2012. This physician-designed and led campaign focuses on developing ‘top 5 lists’ of tests, treatments and procedures in various specialties that were deemed to be unnecessary and potentially harmful.5 This campaign now has 60 US specialty societies, but participation is growing internationally, including Canada, the Netherlands, Italy, Japan and others. A major question asked by clinicians, health policy experts and payers (either government or health insurers) is: what is the prevalence of low-value care in clinical practice? Colla and colleagues have examined the question in this study. 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 | 0.006 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 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".