A comparison of quantitative coronary angiography systems using a unique set of in vivo coronary stenosis images.
Bibliographic record
Abstract
OBJECTIVE: To compare the accuracy and precision of measurements of minimum lumen diameter (MLD) among two existing, and one new, quantitative coronary angiography systems. MATERIALS AND METHODS: The analysis was performed using in vivo cinearteriograms of precisely drilled, radiolucent plastic beads that were inserted percutaneously into the coronary arteries of canines. The existing algorithms compared were the ArTrek and the Coronary Measurement System (CMS). The latter was applied in two modes: a mode based on a minimal cost analysis algorithm and a mode based on a gradient field transform. The new algorithm (CorTrek) was also applied in two modes: a mode called the ArTrek compatible mode and a mode known as the 'regression' mode. The latter mode uses a look-up table, based on a phantom calibration step, to readjust the measured MLD to overcome system nonlinearities (overestimation of small diameters and underestimation of large diameters). RESULTS: In the absence of editing, the optimal accuracy (no significant bias) was achieved with the ArTrek compatible algorithm (-0.03 mm for an MLD between 0.83 and 1.83 mm). All other algorithms showed significant under- or overestimation of the MLD within this range. The precision ranged from 0.18 to 0.40 mm without editing, and was best with the ArTrek algorithm. CONCLUSIONS: The present study provides in vivo validation and comparative performance characteristics of a new, accurate coronary quantitative angiography system.
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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 | 0.005 | 0.010 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".