Modulation of coronary sinus pressure decreases microvascular resistances in the right coronary artery and improves symptoms of microvascular angina
Bibliographic record
Abstract
The coronary venous circulation is attributed a marginal role in regulating myocardial perfusion. Evidence suggests that elevation of coronary sinus (CS) pressure can reduce coronary microvascular resistances,1 possibly through the recruitment of previously dormant capillary networks in the territory of the left anterior descending coronary. Microvascular dysfunction may, however, selectively involve the right coronary (RCA) bed, which is not directly tributary of the CS. Microvascular resistances (IMR) and flow reserve (CFR) were assessed in a 64-year-old man with normal ventricular function, Canadian Cardiovascular Society (CCS) angina class 3, no epicardial stenosis, and evidence of inducible ischaemia of the posterolateral wall. Severe microvascular dysfunction with a ‘high resistances’ endotype, most pronounced in the RCA territory, was shown (left panels, normal values: IMR < 25, CFR > 2.5). Measurements were then repeated during temporary CS occlusion with a Swan-Ganz balloon (right panel, *). The resulting increase in CS pressure was associated with a 40% decrease in IMR and a 75% increase in CFR measured in the RCA (right panels, * marks the pressure wire X, Abbott Vascular). It was therefore decided to implant a CS reducer. Four weeks after implantation, the patient reported improved symptoms from CCS class 3 to class 2.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".