Abstract 5585: Understanding Physicians’ Risk Stratification of Acute Coronary Syndromes: Insights from the Canadian ACS II Registry
Bibliographic record
Abstract
An important treatment-risk paradox exists in the management of acute coronary syndromes (ACS). However, the process of risk stratification by physicians and its relationship to patient management have not been well studied. Our objective was to examine patient risk assessment by physician in relation to treatment and objective risk score evaluation, and the underlying patient characteristics that physicians consider to indicate high risk. The prospective Canadian ACS II Registry recruited 1956 patients admitted for non-ST elevation ACS in 36 hospitals in Oct 2002-Dec 2003. Patient risk assessment by the treating physician and management were recorded on standardized case report forms. We calculated the TIMI, PURSUIT and GRACE risk scores for each patient. Of the 1956 ACS patients, 347 (17.8%) patients were classified as low risk, 822 (42%) as intermediate risk, and 787 (40.2%) as high risk by their treating physicians. Patients considered as high risk were more likely to receive aggressive medical therapies and to undergo coronary angiography and revascularization. However, there were only weak correlations (Kendall’s tau-b correlation coefficients ranging from 0.08 to 0.14) between risk assessment by physicians and all 3 validated risk scores. Advanced age was an independent negative predictor. Furthermore, there was no significant association between the high risk category and several established prognosticators, such as history of heart failure, hemodynamic variables, and creatinine. Contemporary risk stratification of ACS appears suboptimal and may perpetuate the treatment-risk paradox. Physicians may not recognize and incorporate the most powerful adverse prognosticators into overall patient risk assessment. Routine use of validated risk score may enhance risk stratification and facilitate more appropriate tailoring of intensive therapies towards high-risk patients.
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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 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 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".