Lesion characteristics and coronary stent selection with computed tomographic coronary angiography: a pilot investigation comparing CTA, QCA and IVUS.
Bibliographic record
Abstract
OBJECTIVE: The accurate assessment of a target coronary lesion and appropriate stent selection is important in ensuring procedural success during percutaneous coronary intervention (PCI). Though quantitative coronary angiography (QCA) and intravascular ultrasound (IVUS) are available, stent selection is most commonly performed by visual estimation alone. Computed tomographic coronary angiography (CTA) has been shown to correlate well with QCA and IVUS in the assessment of coronary stenoses and may also have a role in stent guidance. MATERIALS AND METHODS: Patients awaiting elective PCI underwent CTA. Blinded observers assessed lesion characteristics using: CTA, QCA, IVUS and visual estimation. Luminal diameters, lesion lengths, ACC/AHA lesion types and CTA-suggested stent sizes were compared. RESULTS: A total of 17 patients (26 lesions) were evaluated. There was good correlation between CTA and IVUS for luminal diameter and for lesion length (r = 0.86 and 0.71, respectively). Similarly, the inter-test variability between the two methods using the intra-class coefficient (ICC = 0.85) was similar to the inter-observer variability of IVUS (ICC = 0.90). The agreement between CTA and visual estimation for lesion type was good (K = 0.79) and was similar to the agreement between the two visual observers (K = 0.72). There was good correlation between CTA stent recommended and actual stent selected (diameter, r = 0.82; length, r = 0.64). CONCLUSIONS: If CTA data is available prior to coronary angioplasty, the reporting of luminal size, length, and lesion type may assist the clinician with coronary stent selection.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".