Comparison of Qualitative and Quantitative Assessments of Iliac Artery Tortuosity and Calcification
Bibliographic record
Abstract
INTRODUCTION: During endovascular aneurysm repair, the iliac artery typically serves as a conduit for device delivery. The degree of tortuosity and calcification in the iliac artery ultimately determines whether the device can successfully traverse the vessel. These 2 parameters can be assessed using qualitative approaches or calculated using quantitative methods based on the Society for Vascular Surgery (SVS) reporting standards. The objective of this study was to determine whether qualitative methods are sufficient to accurately assess iliac artery tortuosity and calcification by calculating interobserver variability and comparing them to the SVS Reporting Standards. METHODS: Three vascular surgeons reviewed preoperative computed tomography scans for 50 patients who underwent fenestrated endovascular aneurysm repair and qualitatively assessed left and right iliac artery tortuosity and calcification. Iliac artery geometries were segmented from these image sets. Tortuosity index and calcification length ratio were calculated and categorized based on the SVS Reporting Standards. RESULTS: Interobserver variability was calculated for the qualitative assessments using interclass correlation coefficients. For tortuosity index, among the 3 observers, good agreement was found for the left iliac artery and fair agreement was found for the right. For calcification length ratio, excellent agreement was found for both iliac arteries. When compared to the quantitative assessment, the qualitative assessments underpredicted tortuosity in 2.3% of cases, matched the quantitative values in 16.7% of cases, and overpredicted tortuosity in 81.0% of cases. The qualitative assessments underpredicted calcification in 46.3% of cases, matched the quantitative values in 49.3% of cases, and overpredicted calcification in 4.3% of cases. CONCLUSION: Qualitative assessment of iliac artery tortuosity showed fair-to-good interobserver agreement and poor agreement to SVS Reporting Standards. Qualitative assessment of iliac artery calcification showed excellent interobserver agreement and fair agreement to SVS Reporting Standards. These trends should be considered when qualitative reporting methodologies are used.
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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 | 0.014 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".