Cervical Internal Carotid Occlusion versus Pseudo-occlusion at CT Angiography in the Context of Acute Stroke: An Accuracy, Interobserver, and Intraobserver Agreement Study
Bibliographic record
Abstract
Purpose To evaluate the diagnostic accuracy and reliability of computed tomographic (CT) angiography to distinguish true cervical internal carotid artery (ICA) occlusion from pseudo-occlusion (defined as an isolated intracranial thrombus that impedes ascending blood flow) in the context of acute stroke. Materials and Methods This was a retrospective study of patients who underwent thrombectomy with preprocedural CT angiography that helps to demonstrate a lack of attenuation in the cervical ICA on the symptomatic side (24 men and 13 women; mean age, 63 years; age range, 30–86 years). Seven readers, including five neuroradiologists and two interventional neuroradiology fellows, independently reviewed the CT angiography images to assess whether there was true cervical ICA occlusion. Their results were compared with digital subtraction angiography (DSA) as the reference standard. Sensitivity and specificity for detecting true occlusion as well as accuracy and diagnostic odds ratio were calculated, with inter- and intraobserver κ statistics. Results Cervical ICA pseudo-occlusion occurred in 12 of 37 patients (32.4%) with nonattenuation of the cervical ICA on the symptomatic side. Interobserver agreement coefficients did not reach the substantial value of 0.61 for either pairs or groups of readers. The cohort’s average sensitivity and specificity was 68% (95% confidence interval [CI]: 59%, 76%) and 75% (95% CI: 71%, 80%), respectively, with a diagnostic odds ratio of 8 (95% CI: 3, 18) and only fair interobserver agreement (κ = 0.32; 95% CI: 0.16, 0.47). Conclusion In the context of acute ischemic stroke with ipsilateral ICA nonattenuation at single-phase CT angiography, even specialized radiologists may not reliably distinguish true cervical occlusion from pseudo-occlusion. © RSNA, 2017 Online supplemental material is available for this article.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".