Clinical uncertainty in large vessel occlusion ischemic stroke: does automated perfusion imaging make a difference? An intra-rater and inter-rater agreement study
Bibliographic record
Abstract
Background Limited research exists regarding the impact of neuroimaging on endovascular thrombectomy (EVT) decisions for late-window cases of large vessel occlusion (LVO) stroke. Objective T0 assess whether perfusion CT imaging: (1) alters the proportion of recommendations for EVT, and (2) enhances the reliability of EVT decision-making compared with non-contrast CT and CT angiography. Methods We conducted a survey using 30 patients drawn from an institutional database of 3144 acute stroke cases. These were presented to 29 Canadian physicians with and without perfusion imaging. 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 ). Results The percentage of EVT recommendations differed by 1.1% with or without perfusion imaging. Individual decisions changed in 21.4% of cases (11.3% against EVT and 10.1% in favor). Inter-rater agreement (κ G ) among the 29 raters was similar between non-perfusion and perfusion CT neuroimaging (κ G =0.487; 95% CI 0.327 to 0.647 and κ G =0.552; 95% CI 0.430 to 0.675). The 95% CIs overlapped with moderate agreement in both. Intra-rater agreement exhibited overlapping 95% CIs for all 28 raters. κ G was either substantial or excellent (0.81–1) for 71.4% (20/28) of raters in both groups. Conclusions Despite the minimal difference in overall EVT recommendations with either neuroimaging protocol one in five decisions changed with perfusion imaging. Regarding agreement we found that the use of automated CT perfusion images does not significantly impact the reliability of EVT decisions for patients with late-window LVO.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".