Extremely tortuous coronary arteries - when optical coherence tomography and fractional flow reserve did not help us much
Bibliographic record
Abstract
Introduction. Extreme coronary tortuosity may lead to flow alteration resulting in a reduction in coronary pressure distal to the tortuous segment, subsequently leading to ischemia. Therefore the detection of a true cause of ischemia, i.e. whether a fixed stenosis or tortuosity by itself is responsible for its creation, with non-invasive and invasive methods is a real challenge. Case report. We presented a case of a patient with a history of stable angina [Canadian Cardiovascular Society (CCS class II)], an abnormal stress test and coronary tortuosity without hemodynamically significant stenosis. Due to suspected linear lesion between the two bends in proximal segment of Right coronary artery (RCA) we performed optical coherence tomography (OCT), minimum lumen area (MLA)-13.19 mm2) and fractional flow reserve (FFR) RCA (0.94). We opted for conservative treatment for stable angina. Conclusion. When tortuosities are associated with atherosclerosis in coronary artery for determination of true cause of ischemia invasive methods can be used, such as OCT and FFR. This article has been corrected. Link to the correction 10.2298/VSP1912304E
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".