Interrater variability for CT angiography evaluation between neurologists and neuroradiologist in acute stroke patients
Bibliographic record
Abstract
OBJECTIVE: The diagnosis of arterial occlusion has a considerable impact on the indication of mechanical thrombectomy, and CT angiography (CTA) is recommended in the management of acute stroke. The goal of the present study is to assess the interrater agreement in the diagnosis of occlusion of intracranial arteries on CTA between a neuroradiologist and neurologists. METHODS: CTA images of 75 acute stroke patients were evaluated for occlusion of intracranial arteries by an experienced interventional neuroradiologist, and stroke and general neurologists. RESULTS: 75 patients who were treated by intravenous thrombolysis were enrolled in the study. CTA images were available for all 75 patients (34 females; mean age ± SD, 72 ± 14 years; National Institutes of Health Stroke Scale 10; median 8-14; and Alberta Stroke Program Early CT mean 9.7). The agreement between the neuroradiologist and neurologists in evaluation of intracranial artery occlusion was as follows: occlusion of the middle cerebral artery segment M1: observer agreement 77%, kappa (κ) = 0.61 and middle cerebral artery M2: observer agreement 77%, κ 0.48; internal carotid artery: observer agreement 92%, κ 0.84; T occlusion: observer agreement 90.0%, κ 0.33; posterior cerebral artery segments P1 and P2: observer agreement 98%, κ 0.97; basilar artery: observer agreement 96%, κ 0.92; and vertebral artery segment V4: observer agreement 88%, κ 0.48. CONCLUSION: Interrater agreement of CTA evaluation of occlusion between the neurologists and the neuroradiologist was very strong. The ability of the trained neurologists to read an intracranial large vessel occlusion correctly may improve the door-to-needle times in acute stroke. Advances in knowledge: In this study, the neurologists were able to recognize occlusion of intracranial arteries. This could accelerate the management of acute stroke care.
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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 | 0.031 | 0.123 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".