Canadian Cardiovascular Society classification of effort angina: an angiographic correlation
Bibliographic record
Abstract
BACKGROUND: The Canadian Cardiovascular Society classification (CCSC) remains the standard for grading angina in patients with chronic stable angina. The utility value of this angina grading system in predicting the severity of coronary artery disease is not clear. AIM: We studied the relationship between the clinical angina grade and the angiographic severity of underlying coronary artery disease. MATERIALS AND METHODS: The participants in the study were 493 patients with stable angina who had undergone coronary angiography from 1998 to 2001. They were grouped according to their anginal grading and the number of vessels diseased. Significant lesions were defined as 50% narrowing for the left main and 70% for the left and right coronaries and their major branches. STATISTICAL ANALYSIS: The chi2-test was used for statistical analysis and a P-value <0.05 was taken as significant. RESULTS: There was no significant difference between the four angina class patients and the incidence of single-, double- and triple-vessel involvement. Class 1 patients had less left main trunk disease than class 4 patients. Class 3 and 4 patients had significantly fewer normal coronary angiograms. CONCLUSIONS: There is generally little correlation between coronary artery disease and the CCSC of effort angina except for left main disease. Presence or absence of angina rather than the CCSC should indicate the need for coronary angiography.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".