Clinical expert consensus document on standards for acquisition, measurement and reporting of intravascular ultrasound regression/progression studies
Bibliographic record
Abstract
Atherosclerotic cardiovascular disease is a leading cause of morbidity and mortality despite the widespread use of established medical therapies. This has prompted the search to identify new therapeutic approaches to achieve more effective prevention of cardiovascular events. Considerable interest has focused on the role of surrogate markers of therapeutic efficacy in the early evaluation of novel anti-atherosclerotic therapies. Monitoring changes in the extent of coronary atherosclerosis with intravascular ultrasound (IVUS) has been increasingly employed in clinical trials to assess progression and regression of atherosclerosis. This is based on the pivotal role that atherosclerotic plaque plays in the natural history of cardiovascular disease and the acceptance of validated arterial imaging approaches including coronary angiography and carotid intimal-medial thickness by regulatory authorities. The ability to generate high-resolution imaging of the entire thickness of the coronary artery wall permits evaluation of the entire burden of atherosclerotic plaque. In order to understand the differences, similarities, limitations and pitfalls of the IVUS technique among different academic core laboratories, a number of meetings of representatives from these groups were convened in 2007 and 2008. This document is the result of those IVUS methodology meetings that assembled experts from core laboratories to discuss standards for image acquisition, definitions, criteria, analyses, and primary and secondary endpoints.
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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 | 0.120 | 0.140 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.015 | 0.004 |
| Research integrity | 0.016 | 0.010 |
| Insufficient payload (model declined to judge) | 0.009 | 0.012 |
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".