Collateral formation in patients after percutaneous myocardial revascularization: a mechanism for improvement?
Bibliographic record
Abstract
BACKGROUND: Percutaneous myocardial revascularization (PMR) has emerged as a novel therapeutic strategy for patients with ischemic heart disease not amendable to traditional revascularization approaches. Despite the symptomatic improvement documented in clinical trials, underlying mechanism remains unknown. METHODS: We assessed the coronary angiograms of 25 patients before and 6 months after the PMR procedures for collateral vessel formation. The Rentrop scoring system was used to score the extent of collateral vessel formation. The angiograms were analyzed by 2 independent reviewers. The change in Rentrop score and improvement in angina status was correlated. RESULTS: During follow-up period of 6 months, no patient died or suffered from myocardial infarction. Overall the mean Canadian Classification Society (CCS) class among the study patients was significantly improved from 3.5 to 2.4; mean difference = 1.08 (p < 0.0001). Among these, 8 (32%) had improvement of > 2 CCS classes (p = 0.003) (improvement group). The rest of the patients had either no change (n = 8; 32%) or improvement of 1 CCS class (n = 9; 36%) (no improvement group). The Rentrop score improved in 3 (12%), remained unchanged in 22 (88%) and deteriorated in 0 patient. Overall, there was no significant change in Rentrop score at 6-month follow-up; mean difference 0.13 (p = 0.08). There was also no correlation between the change in extent of collateral vessel formation and the improvement in angina status (correlation coefficient r = 0.09) at 6 months after PMR. CONCLUSION: Improvement in collateral vessel formation is unlikely to be a mechanism responsible for symptomatic improvement in patients after PMR.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".