Role Of Intra Coronary Imaging And Physiology In Diagnosis And Management Of Coronary Artery Disease.
Bibliographic record
Abstract
Coronary artery disease (CAD) is the leading cause of death in the Indo-Pakistan subcontinent as well as globally. Coronary angiography is considered the gold standard test for the diagnosis of CAD. Therefore, an accurate interpretation of coronary angiography is of paramount importance in decision-making to treat patients with CAD. Coronary angiography has the inherent limitation of being a two-dimensional X-Ray lumenogram of a complex three-dimensional vascular structure. Visual assessment of angiogram can lead to both inter- and intra-observer variability in the assessment of the severity and extent of the disease which can lead to differences in management strategies. This issue becomes even more relevant when assessing left main stem (LMS), bifurcations, diffuse coronary artery disease or situations involving complex coronary morphology. Interventional cardiology has been revolutionised by recent advances in techniques, and innovative technologies in the catheterisation laboratory. Today, a modern catheterisation laboratory is equipped with adjunctive technologies, such as Quantitative Coronary Angiography (QCA), Fractional Flow Reserve (FFR), Intra-Vascular Ultra-Sonography (IVUS), and Optical Coherence Tomography (OCT), to help clinicians make a well-informed decision based on detailed anatomical and physiological assessment of a coronary artery rather than judgement based solely on visual assessment. In this article, we have briefly described the utility and evidence behind these adjunctive modalities and have provided examples of clinical cases to highlight their use in aiding physicians to make a well-informed treatment decision.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".