NASCET Percent Stenosis Semi-Automated Versus Manual Measurement on CTA
Bibliographic record
Abstract
PURPOSE: To compare North American Symptomatic Carotid Endarterectomy Trial (NASCET) stenosis values and NASCET grade categorization (mild, moderate, severe) of semi-automated vessel analysis software versus manual measurements on computed tomography angiography (CTA). METHODS: There were four observers. Two independently 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 of these two observers performed this task twice on each carotid, the second analysis was 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. The calculated NASCET stenoses were categorized into mild, moderate, or severe. Chi square and analysis of variance (ANOVA) were used to test for statistical differences. RESULTS: ANOVA did not find a statistically significant difference in the mean percent stenosis when comparing the two manual measurements, the two semi-automated measurements, and the repeat semi-automated. Chi square demonstrated that the distribution of grades of stenosis were statistically different (p<0.05) between the manual and semiautomated grades. Semi-automated vessel analysis tended to underestimate the degree of stenosis compared to manual measurement. CONCLUSION: The mean percentage stenosis determined by semi-automated vessel analysis is not significantly different from manual measurement. However, when the data is categorized into mild, moderate and severe stenosis, there is a significant difference between semi-automated and manual measurements. The semi-automated software tends to underestimate the stenosis grade compared to 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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".