A cross-sectional multicentre study of cardiac risk score use in the management of unstable angina and non-ST-elevation myocardial infarction
Bibliographic record
Abstract
OBJECTIVES: Quantitative risk assessment in unstable angina (UA) and non-ST-elevation myocardial infarction (NSTEMI), by using cardiac risk scores, is recommended in international guidelines. However, a gap between recommended care and actual practice exists, as these instruments seem underused in practice. The present study aimed to determine the extent of cardiac risk score use and to study factors associated with lower or higher cardiac risk score use. SETTING: 13 hospitals throughout the Netherlands. PARTICIPANTS: A retrospective chart review of 1788 charts of patients with UA and NSTEMI, discharged in 2012. PRIMARY AND SECONDARY OUTCOMES: The extent of cardiac risk score use reflected in a documented risk score outcome in the patient's chart. Factors associated with cardiac risk score use determined by generalised linear mixed models. RESULTS: In 57% (n=1019) of the charts, physicians documented the use of a cardiac risk score. Substantial variation between hospitals was observed (16.7-87%), although this variation could not be explained by the presence of on-site revascularisation facilities or a hospitals' teaching status. Obese patients (OR=1.49; CI 95%1.03 to 2.15) and former smokers (OR=1.56; CI 95%1.15 to 2.11) were more likely to have a cardiac risk score documented. Risk scores were less likely to be used among patients diagnosed with UA (OR=0.60; CI 95% 0.46 to 0.77), in-hospital resuscitation (OR=0.23; CI 95% 0.09 to 0.64), in-hospital heart failure (OR=0.46; CI 95% 0.27 to 0.76) or tachycardia (OR=0.45; CI 95% 0.26 to 0.75). CONCLUSIONS: Despite recommendations in cardiac guidelines, the use of cardiac risk scores has not been fully implemented in Dutch practice. A substantial number of patients did not have a cardiac risk score documented in their chart. Strategies to improve cardiac risk score use should pay special attention to patient groups in which risk scores were less often documented, as these patients may currently be undertreated.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".