Importance of collateral circulation in coronary heart disease
Bibliographic record
Abstract
AIMS: Collateral arteries are a common but inconsistent finding in coronary heart disease (CHD). We endeavoured to review the methods for coronary artery collateral assessment, the predictors and clinical importance of collateral blood flow, and the potential for therapeutic augmentation of collateral anastomoses. METHODS AND RESULTS: While many methods have been used to assess collateral blood flow only a few have been formally validated. Collateral flow index, as determined by measurement of intra-coronary pressure or flow velocity, is the most robust measure of collateral flow. These techniques have led to important advances in our understanding of collateral artery function. Coronary collateral arteries may prevent myocardial ischaemia in healthy subjects and in patients with CHD. A functional collateral circulation may lead to reduced ischaemia, preservation of ventricular function, and an improved prognosis. Recent trials have demonstrated that vascular progenitor cell therapies may improve ventricular function following acute myocardial infarction, raising the possibility of effective biological treatments to improve myocardial blood flow and prognosis in CHD. CONCLUSIONS: Coronary collateral anastomoses represent a prognostically important adaptive response in patients with CHD. Therapeutic augmentation of collaterals with emerging biological therapies represents a desirable goal for treating CHD 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".