Atherosclerosis diagnostic imaging by optical spectroscopy and optical coherence tomography
Bibliographic record
Abstract
Atherosclerosis is traditionally viewed as a disease of uncontrolled plaque growth leading to arterial occlusion. More recently, however, occlusion of the arterial lumen is being viewed as an acute event triggered by plaque rupture and thrombosis. An atheromatous plaque becomes vulnerable to sudden activation and/or rupture when a constellation of processes are activated by various trigger mechanisms. There is growing evidence that the vulnerability (i.e. susceptibility to rupture) and thrombogenic nature of the plaque need to be taken into account in the planning and treatment of the disease. X-ray fluoroscopy and intravascular ultrasound, the current clinical diagnostic tools are not capable of the providing a complete histological picture of the plaque region. Intravascular diagnostic imaging of coronary atherosclerotic plaques by optical means to assess plaque, patient risk and assist in planning treatment strategies represents the future in angioplasty treatment by interventional cardiologists. The techniques which will enable a clinically acceptable and reliable intravascular diagnostic platform are currently being investigated and compared to the clinical standard of histology. Currently, we are investigating the use of a number of optical and imaging techniques for biochemical analysis of arterial tissue including Raman, near infrared and fluorescence spectroscopies. Biochemical imaging will provide compositional information on collagen, elastin, lipid and thrombogenic by-products as well as gauging inflammation and tissue remodeling activity levels. To complement the functional biochemical imaging, optical coherence tomography will be provide structural morphological imaging. The synergistic combination of functional and structural imagery will provide the interventional cardiologist with a complete clinical picture of the atherosclerotic plaque region. The clinician can use this diagnostic information to plan a personalized treatment procedure based on the entire clinical presentation.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".