Interpretation Errors in CT Angiography of the Head and Neck and the Benefit of Double Reading
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.043 | 0.294 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".