Interobserver Agreement in Scoring Angiographic Results of Basilar Artery Occlusion Stroke Therapy
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The modified TICI Infarction grading system is a metric currently used to evaluate angiographic results of thrombectomy for large-vessel occlusion in ischemic stroke. Originally designed for evaluating MCA territories, it is currently used for other vessel occlusions, including the posterior circulation. We postulate that the modified TICI use for the posterior circulation is not accurate due to the different vascular territories supplied by vertebrobasilar vasculature, making grading more complex. MATERIALS AND METHODS: We collected angiographic results from 30 patients who presented with acute posterior circulation occlusions between 2015 and 2018 and underwent thrombectomy in our institution. Eight observers were asked to evaluate the TICI scores before and after thrombectomy. The multirater statistics were computed using Fleiss κ analysis. Further data were collected regarding the potential brain territories at risk and the existence of atherosclerotic disease in the basilar artery. RESULTS: The overall agreement κ reached 0.277 (SD, 0.013), which suggests a "fair" agreement among the raters. On average, 45% of observers achieved a high accuracy in predicting brain areas at risk of ischemia. As for the existence of basilar atherosclerotic disease, a high agreement (defined as at least 5 of 6 observers) was seen in 20 of the 30 patients. CONCLUSIONS: Despite TICI being ubiquitous in stroke diagnostics, the high variability of posterior circulation TICI scores calls into question its use in these strokes. Other methods should be developed to assess recanalization in the posterior circulation.
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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 | 0.077 | 0.131 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 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 | 0.000 |
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".