Reproducibility of Semi-Automated Measurement of Carotid Stenosis on CTA
Bibliographic record
Abstract
PURPOSE: To compare the reproducibility of semi-automated vessel analysis software to manual measurement of carotid artery stenosis on computed tomography angiography (CTA). METHODS: Two observers separately analyzed 81 carotid artery CTAs using semi-automated vessel analysis software according to a blinded protocol. The software measured the narrowest stenosis in millimeters (mm), distal internal carotid artery (ICA) in mm, and calculated percent stenosis based on NASCET criteria. One observer performed this task twice on each carotid, the second analysis delayed two months in order to mitigate recall bias. Two other observers manually measured the narrowest stenosis in mm, distal ICA in mm, and calculated NASCET percent stenosis in a blinded fashion. Correlation coefficients were calculated for each group comparing the narrowest stenosis in mm, distal ICA in mm, and NASCET percent stenosis. RESULTS: The semi-automated vessel analysis software provided excellent intraobserver correlation for narrowest stenosis in mm, distal ICA in mm, and NACSET percent stenosis (Pearson correlation coefficients of 0.985, 0.954, and 0.977 respectively). The semi-automated vessel analysis software provided excellent interobserver correlation (0.925, 0.881, and 0.892 respectively). The interobserver correlation for manual measurement was good (0.595, 0.625, and 0.555 respectively). There was a statistically significant difference in the interobserver correlation between the semi-automated vessel analysis software observers and the manual measurement observers (P < 0.001). CONCLUSION: Semi-automated vessel analysis software is a highly reproducible method of quantifying carotid artery stenosis on CTA. In this study, semi-automated vessel analysis software determination of carotid stenosis was shown to be more reproducible than manual measurement.
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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 | 0.016 | 0.059 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".