Clinical Uncertainty In Large Vessel Occlusion Ischemic Stroke (CULVO): An Intrarater And Interrater Agreement Study
Bibliographic record
Abstract
Abstract Background Limited research exists regarding the impact of neuroimaging modality on endovascular thrombectomy (EVT) decisions for late window large vessel occlusion (LVO) stroke cases. Purpose This study assesses whether perfusion CT imaging: 1) alters the proportion of recommendations for EVT, and 2) enhances the reliability of EVT decision-making compared to non-contrast CT and CT angiography. Materials and Methods We conducted an online survey using 30 patients drawn from an institutional database of 3144 acute stroke cranial CT scans. These cases were presented to 29 stroke or neurointerventional physicians from Canada across two sessions. Physicians evaluated each patient both with and without perfusion imaging and gave EVT recommendations. We used non-overlapping 95% confidence intervals and difference in agreement classification as criteria to suggest a difference between the Gwet AC1 statistics (κ G ). Our outcomes were: 1) the proportion of EVT recommendations, and 2) interrater and intrarater agreement, with or without perfusion imaging. Results In the first round, 29 raters completed the assessment, with 28 finishing the second round. The percentage of EVT recommendations differed by 1.1% with or without perfusion imaging. However, individual decisions changed in 21.4% of cases, with 11.3% against EVT and 10.1% in favor. Interrater agreement (κG) among the 29 raters was similar between non-perfusion CT neuroimaging and perfusion CT neuroimaging (κG = 0.487; 95% CI 0.327, 0.647 and κG = 0.552; 95% CI 0.430, 0.675). The 95% CIs overlapped with moderate agreement in both. Intrarater agreement exhibited overlapping 95% CIs for all 28 raters. κG was either substantial or excellent (0.81-1) for 71.4% (20/28) of raters in both groups. Conclusion The difference in EVT recommendations is minimal with either neuroimaing protocol. Regarding agreement we found that use of automated CT perfusion images does not significantly impact the reliability of EVT decisions for late window LVO patients.
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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 | 0.088 | 0.248 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".