Standardized Outcome Measurement for Patients With Coronary Artery Disease: Consensus From the International Consortium for Health Outcomes Measurement (ICHOM)
Bibliographic record
Abstract
BACKGROUND: Coronary artery disease (CAD) outcomes consistently improve when they are routinely measured and provided back to physicians and hospitals. However, few centers around the world systematically track outcomes, and no global standards exist. Furthermore, patient-centered outcomes and longitudinal outcomes are under-represented in current assessments. METHODS AND RESULTS: The nonprofit International Consortium for Health Outcomes Measurement (ICHOM) convened an international Working Group to define a consensus standard set of outcome measures and risk factors for tracking, comparing, and improving the outcomes of CAD care. Members were drawn from 4 continents and 6 countries. Using a modified Delphi method, the ICHOM Working Group defined who should be tracked, what should be measured, and when such measurements should be performed. The ICHOM CAD consensus measures were designed to be relevant for all patients diagnosed with CAD, including those with acute myocardial infarction, angina, and asymptomatic CAD. Thirteen specific outcomes were chosen, including acute complications occurring within 30 days of acute myocardial infarction, coronary artery bypass grafting surgery, or percutaneous coronary intervention; and longitudinal outcomes for up to 5 years for patient-reported health status (Seattle Angina Questionnaire [SAQ-7], elements of Rose Dyspnea Score, and Patient Health Questionnaire [PHQ-2]), cardiovascular hospital admissions, cardiovascular procedures, renal failure, and mortality. Baseline demographic, cardiovascular disease, and comorbidity information is included to improve the interpretability of comparisons. CONCLUSIONS: ICHOM recommends that this set of outcomes and other patient information be measured for all patients with CAD.
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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 | 0.380 | 0.363 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.008 | 0.012 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.001 | 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".