Defining the relationship between atherosclerotic plaque, ischaemia, and risk—the story unfolds
Bibliographic record
Abstract
This editorial refers to ‘Quantitative plaque features from coronary computed tomography angiography to identify regional ischemia bymyocardial perfusion imaging’, by Diaz-Zamudio M et al . doi: 10.1093/ehjci/jew274. ‘The real voyage of discovery consists not in seeking new landscapes, but in having new eyes’.—Marcel ProustThe last 50 years has seen tremendous development in the field of non-invasive imaging to aid in the diagnosis and to guide the management of coronary artery disease (CAD).1,2 The rapidity of development has often resulted in a significant lag between the integration of a technology and the realization by the field of all its potential implications and the information that it provides. Nuclear myocardial perfusion testing has been used for decades to help with the diagnosis of CAD and has been shown to provide important prognostic information and to guide medical therapy and revascularization in a fashion that can help improve clinical outcomes in large non-randomized registries.3 Coronary CT angiography has been introduced in the last 15 years and has asserted itself as the non-invasive anatomical gold standard for the evaluation and diagnosis of CAD. CT has not only been shown to provide stenoses assessment that correlate well with ICA, but morphological features of atherosclerotic plaque features such as low-density plaque, outward expansion, and napkin ring sign have been identified as independently predicted of downstream risk beyond stenosis.2,4 Most recently, non-invasive fractional flow reserve (FFR) derived through complex computer modelling and computational fluid dynamics from resting coronary …
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.008 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".