Abstract 15794: Role of Endothelial Shear Stress and Endothelial Shear Stress Gradient in Plaques Associated With Acute Erosion vs. Stable Control Plaques
Bibliographic record
Abstract
Introduction: The role of endothelial shear stress (ESS) in the natural history of plaque growth and TCFA formation/destabilization has been studied, but the role in plaque erosion is unknown. High ESS gradient (ESSG) has been hypothesized to promote plaque erosion, but no studies have included matched “control” stable plaques with the same minimal luminal area (MLA) and reference luminal area (RLA) but no adverse coronary event. Hypothesis: To compare ESS and ESSG between coronary plaques that developed erosion and similar morphology plaques that remain stable. Methods: We studied a subset of patients from both TOTAL and COMPLETE trials who underwent angiography and OCT evaluation: 27 patients (27 arteries: 18 LAD, 3 LCX, 6 RCA). Plaques were divided into Plaque Erosion (n=16) from TOTAL study with OCT features of plaque erosion and Control (n=11) plaques (non-culprit lesions from COMPLETE) with matched MLA and RLA and no OCT evidence of plaque disruption. Orthogonal angiographic views were used to generate a 3-D arterial reconstruction, and angio centerline was merged with OCT centerline. Local ESS distribution was assessed by computational flow dynamics and reported in consecutive 3-mm segments. Results: Table 1 shows differences in ESS between Plaque Erosion and Control Plaques Conclusions: In coronary plaques with similar severe obstruction (MLA) and reference area (RLA), plaque erosion is associated with higher coronary flow, max ESS, and ESSG in any direction, in the proximal-to-distal direction, and in the circumferential direction compared to plaques that remain stable. Future studies will determine which "feature (s)" of high ESS or ESSG are independently associated with erosion.
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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) | 0.000 | 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) | 0.004 | 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".