Carotid near-occlusion is often overlooked when CT angiography is assessed in routine practice
Bibliographic record
Abstract
OBJECTIVE: Assess the sensitivity and specificity of computed tomography angiography (CTA) for carotid near-occlusion diagnosis interpreted in clinical practice against expert assessment. METHODS: CTAs were graded by two expert interpreters for near-occlusion. Findings were compared with clinical reports in 383 consecutive cases with symptomatic ≥ 50% carotid stenosis. In addition, 14 selected CTA exams (8 near-occlusions and 6 controls) were analyzed in a national effort by 13 radiologists experienced with carotid CTA. RESULTS: In clinical practice, imaging reports were 20% (95% CI 12-28%) sensitive for near-occlusion, ranging 0-58% between different radiologists; specificity was 99%. Among the 13 radiologists reviewing the same 8 near-occlusions, the average sensitivity was 8%, ranging 0-75%; specificity was 100%. CONCLUSIONS: Carotid near-occlusion is systematically under-reported in clinical routine practice, caused by limited application of grading criteria when assessing CTA. KEY POINTS: • Carotid near-occlusion is severe stenosis with distal artery collapse; this collapse is often subtle. • A fifth of near-occlusions were detected in routine practice. Many readers mistake near-occlusion for stenosis without distal artery collapse, either by not actively searching for subtle collapses or by not interpreting the collapse correctly when noticed. • On the other hand, the novice diagnostician should be cautioned to not over-diagnose near-occlusion; other causes of extracranial ICA asymmetry also exist such as distal disease and Circle of Willis anatomical variants.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 teacher head, 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".