Assessment of myocardial ischaemia and viability: role of positron emission tomography
Bibliographic record
Abstract
In developed countries, coronary artery disease (CAD) continues to be a major cause of death and disability. Over the past two decades, positron emission tomography (PET) imaging has become more widely accessible for the management of ischemic heart disease. Positron emission tomography has also emerged as an important alternative perfusion imaging modality in the context of recent shortages of molybdenum-99/technetium-99m ((99m)Tc). The clinical application of PET in ischaemic heart disease falls into two main categories: first, it is a well-established modality for evaluation of myocardial blood flow (MBF); second, it enables assessment of myocardial metabolism and viability in patients with ischaemic left ventricular dysfunction. The combined study of MBF and metabolism by PET has led to a better understanding of the pathophysiology of ischaemic heart disease. While there are potential future applications of PET for plaque and molecular imaging, as well as some clinical use in inflammatory conditions, this article provides an overview of the physical and biological principles behind PET imaging and its main clinical applications in cardiology, namely the assessment of MBF and metabolism.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".