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.
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 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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".