Investigation of Arterial Geometry as a Local Risk Factor for Carotid Atherosclerosis
Bibliographic record
Abstract
There is little doubt that disturbed hemodynamic forces play a role in the development of focal atherosclerotic lesions; however, these forces are difficult to measure directly. Instead, it has been proposed that artery geometry, as the primary determinant of local hemodynamics, could be a clinically feasible surrogate “local” risk factor for atherosclerosis. To date this hypothesis has not been satisfactorily tested, owing to superficial geometric surrogates of disturbed flow, small sample sizes (effect of systemic factors) and/or confounding effects of disease on geometry. The primary objective of this thesis was to test this “geometric risk hypothesis” via direct association of definitive geometric factors and an early atherosclerosis marker (e.g. wall thickness), made possible through our access to magnetic resonance imaging and risk factor data from the Atherosclerosis Risk in Communities’ Carotid MRI sub-study. First, it was shown that the 3D geometry of the carotid bifurcation could be characterized rapidly and reliably, even for routine clinical acquisitions. Second, two novel individual geometric variables were proposed, inspired by the influence of flare and tortuosity on flow separation, which were shown to improve the prediction of disturbed flow burden compared to “conventional” shape-based geometric variables. Third, these redefined geometric factors, but not their shape-based counterparts, were shown by multiple regression to be independent predictors of wall thickness, but only after thoroughly accounting for the secondary effects of wall thickening on geometry. These findings provide strong evidence for the geometric risk hypothesis of atherosclerosis in humans group study, and provide important guidance for future investigations of geometric risk; however, the incremental value of optimized geometric risk factors is questionable relative to conventional cardiovascular risk factors, which challenges their future clinical usage as additional non-modifiable local risk factors.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".