Optical coherence tomography in coronary atherosclerosis assessment and intervention
Bibliographic record
Abstract
Since optical coherence tomography (OCT) was first performed in humans two decades ago, this imaging modality has been widely adopted in research on coronary atherosclerosis and adopted clinically for the optimization of percutaneous coronary intervention. In the past 10 years, substantial advances have been made in the understanding of in vivo vascular biology using OCT. Identification by OCT of culprit plaque pathology could potentially lead to a major shift in the management of patients with acute coronary syndromes. Detection by OCT of healed coronary plaque has been important in our understanding of the mechanisms involved in plaque destabilization and healing with the rapid progression of atherosclerosis. Accurate detection by OCT of sequelae from percutaneous coronary interventions that might be missed by angiography could improve clinical outcomes. In addition, OCT has become an essential diagnostic modality for myocardial infarction with non-obstructive coronary arteries. Insight into neoatherosclerosis from OCT could improve our understanding of the mechanisms of very late stent thrombosis. The appropriate use of OCT depends on accurate interpretation and understanding of the clinical significance of OCT findings. In this Review, we summarize the state of the art in cardiac OCT and facilitate the uniform use of this modality in coronary atherosclerosis. Contributions have been made by clinicians and investigators worldwide with extensive experience in OCT, with the aim that this document will serve as a standard reference for future research and clinical application. Optical coherence tomography (OCT) has been widely adopted in research on coronary atherosclerosis and adopted clinically to optimize percutaneous coronary intervention. In this Review, Jang and colleagues summarize this rapidly progressing field, with the aim of standardizing the use of OCT in coronary atherosclerosis.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".