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 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 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 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".