Visual estimation versus different quantitative coronary angiography methods to assess lesion severity in bifurcation lesions
Bibliographic record
Abstract
OBJECTIVES: To compare visual estimation with different quantitative coronary angiography (QCA) methods (single-vessel versus bifurcation software) to assess coronary bifurcation lesions. BACKGROUND: QCA has been developed to overcome the limitations of visual estimation. Conventional QCA however, developed in "straight vessels," has proved to be inaccurate in bifurcation lesions. Therefore, bifurcation QCA was developed. However, the impact of these different modalities on bifurcation lesion severity classification is yet unknown METHODS: From a randomized controlled trial investigating a novel bifurcation stent (Clinicaltrials.gov NCT01258972), patients with baseline assessment of lesion severity by means of visual estimation, single-vessel QCA, 2D bifurcation QCA and 3D bifurcation QCA were included. We included 113 bifurcations lesions in which all 5 modalities were assessed. The primary end-point was to evaluate how the different modalities affected the classification of bifurcation lesion severity and extent of disease. RESULTS: On visual estimation, 100% of lesions had side-branch diameter stenosis (%DS) >50%, whereas in 83% with single-vessel QCA, 27% with 2D bifurcation QCA and 26% with 3D bifurcation QCA a side-branch %DS >50% was found (P < 0.0001). With regard to the percentage of "true" bifurcation lesions, there was a significant difference between visual estimate (100%), single-vessel QCA (75%) and bifurcation QCA (17% with 2D bifurcation software and 13% with 3D bifurcation software, P < 0.0001). CONCLUSIONS: Our study showed that bifurcation lesion complexity was significantly affected when more advanced bifurcation QCA software were used. "True" bifurcation lesion rate was 100% on visual estimation, but as low as 13% when analyzed with dedicated bifurcation QCA software.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".