Use of Cardiac Catheterization for Non–ST-Segment Elevation Acute Coronary Syndromes According to Initial Risk<subtitle>Reasons Why Physicians Choose Not to Refer Their Patients</subtitle>
Bibliographic record
Abstract
BACKGROUND: Despite the recommendation for an early invasive strategy in the treatment of patients who present with non-ST-segment elevation (NSTE) acute coronary syndromes (ACS), referral for cardiac catheterization is suboptimal; the reasons why some patients are not referred remain unclear. METHODS: Patients were recruited into the prospective, observational Canadian ACS Registry II between October 1, 2002, and December 31, 2003; 2136 patients with NSTE ACS identified through the registry were divided into tertiles according to the Thrombolysis in Myocardial Infarction risk score and the rates of catheterization compared. In addition, the most responsible physicians were asked to indicate the main reason they did not refer their patients for catheterization. RESULTS: The rate of referral for catheterization was 64.7%. Patients who underwent catheterization had lower in-hospital (0.8% vs 3.7%; P < .001) and 1-year mortality rates (4.0% vs 10.9%; P < .001) compared with those who did not. Higher-risk patients were referred at a similar rate as low-risk patients (62.5% vs 66.9%; P = .25). Among the reasons provided by the most responsible physician as to why patients were not referred for catheterization, 68.4% of patients were thought to be "not at high enough risk"; however, 59.1% of these patients were found to be at intermediate to high risk according to their baseline Thrombolysis in Myocardial Infarction risk score. CONCLUSIONS: Cardiac catheterization is not used optimally in patients who present with NSTE ACS. Despite better in-hospital and 1-year outcomes in those patients who are referred for catheterization, many higher-risk patients are not being referred because of the perception that they are not at high enough risk. A significant opportunity remains to improve on accurate risk stratification and adherence to an early invasive strategy for higher-risk patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".