Interpretation Errors in CT Angiography of the Head and Neck and the Benefit of Double Reading
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND AND PURPOSE: CTA provides high-resolution imaging of the head and neck vasculature but also of the soft tissues and bones. This results in a large volume of information to be interpreted. This study examines interpretation errors with head and neck CTAs and assesses whether double reading reduces miss rates. MATERIALS AND METHODS: Consecutive CTAs of the neck and intracranial circulation were retrospectively identified and reviewed for vascular and nonvascular findings by a consensus of 2 neuroradiologists. The results were compared with the official report. Significant discrepancies were considered those that would have influenced follow-up or management. RESULTS: We reviewed 503 studies; 144 were originally reported by a staff neuroradiologist alone, 209 by staff and diagnostic radiology resident, and 150 by staff and neuroradiology fellow. Twenty-six significant discrepancies were discovered in 20 studies, corresponding to 4.0% of studies with at least 1 miss, and an overall miss rate per study of 5.2%. There was at least 1 miss in 6.3% of studies interpreted by a staff neuroradiologist alone, 3.3% by staff and resident, and 2.7% by staff and fellow. The miss rate differences were not statistically significant. The most common misses were small aneurysms (50% of misses). CONCLUSIONS: CTA neck and head datasets are now large, and there is a potential for missed findings. Significant discrepancies can occur with a low but not insignificant rate. Arterial pathology accounted for most discrepancies. This study emphasizes the need for careful systematic scrutiny for both vascular and nonvascular pathology regardless of indication. Double reading reduces error rates.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it