Three-Vessel Assessment of Coronary Microvascular Dysfunction in Patients With Clinical Suspicion of Ischemia
Bibliographic record
Abstract
Background— Difficulty directly visualizing the coronary microvasculature as opposed to the epicardial coronary artery makes its assessment challenging. The goal of this study is to measure the index of microcirculatory resistance (IMR) in all 3 major coronary vessels to identify the clinical and angiographic predictors of an abnormal IMR. Methods and Results— Ninety-three patients who underwent coronary physiological assessment in all 3 major coronary vessels were prospectively enrolled (59.8±9.4 years with 77.4% men). IMR was corrected using Yong’s formula and coronary microvascular dysfunction (CMD) was defined using vessel-specific cutoffs. A global IMR was calculated as the sum of the IMR in all 3 major epicardial vessels. Angiographic epicardial disease severity was assessed with vessel-specific and overall SYNTAX score. Median IMR and fractional flow reserve was 17.2 (Q1–Q3: 13.3–22.9) and 0.92 (0.85–0.97). The majority of patients (59.1%) had no CMD, 23.7% had 1-vessel CMD, 14.0% had 2-vessel CMD, and 3.2% had 3-vessel CMD. CMD was observed at a similar rate in the territories supplied by all 3 major coronary vessels (left anterior descending coronary artery 28.0%, left circumflex artery 19.4%, and right coronary artery 23.7%; P =0.39). Fractional flow reserve had a weak, positive correlation with IMR (ρ=0.16; P <0.01). The SYNTAX score had no significant correlation with IMR, both at a patient level (ρ=−0.002; P =0.99) and a vessel-specific level (ρ=−0.06; P =0.36). By multivariable ordinal logistic regression analysis, no variable was left as an independent predictor of an abnormal IMR. Conclusions— Clinical factors and epicardial coronary disease severity are not predictors of the extent of CMD. Clinical Trial Registration— URL: https://www.clinicaltrials.gov . Unique identifier: NCT01621438.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".