<i>In vivo</i>visualization of lipid coronary atheroma with intravascular near-infrared spectroscopy
Bibliographic record
Abstract
INTRODUCTION: Atherosclerotic cardiovascular disease (ASCVD) has become a major health burden and is expected to further increase in the future. Better predictive approaches for ASCVD and more efficacious therapies are required to further improve cardiovascular outcomes. Intravascular imaging has contributed to the elucidation of atherosclerotic mechanisms and evaluation of novel therapies. Near-infrared spectroscopy has enabled the visualization of the lipid extent of atherosclerotic plaques in vivo. Given that lipid accumulation is considered to promote the formation and progression of atherosclerosis, this technology may harbor the potential to identify subjects with high cardiovascular risks and thus adopt more optimized therapeutic approaches. Areas covered: This review will outline the characteristics of NIRS, its validation data and in vivo findings of NIRS imaging in patients with coronary artery disease. The comparisons of NIRS with other imaging modalities will reveal the distinct capability of NIRS imaging to monitor high-risk atheroma harboring lipidic composition. Furthermore, the predictive ability of NIRS-derived measures in the occurrence of ASCVD will be summarized. Expert commentary: Ex vivo and in vivo findings suggest NIRS imaging as a potential tool for cardiovascular risk assessment and monitoring the benefit of pharmacological approaches.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".