Clinical Uncertainty in Large Vessel Occlusion ischemic stroke (CULVO): Does automated perfusion scanning make a difference? Protocol of an intrarater and interrater agreement study
Bibliographic record
Abstract
BACKGROUND: Guidelines recommend the use of perfusion computed tomography (CT) to identify emergent large vessel ischemic stroke (ELVIS) patients who are likely to benefit from endovascular thrombectomy (EVT) if they present within 6-24 hour (late window) of stroke onset. We aim to determine if the interrater and intrarater reliability among physicians when recommending EVT is significantly different when perfusion CT or non-perfusion CT is reviewed. METHODS: A total of 30 non-consecutive patients will be selected from our institutional database comprising 3144 cranial CT scans performed for acute stroke symptoms January 2018 to August 2022. The clinical and radiologic data of the 30 patients will be presented in random order to a group of 29 physicians in two separate sessions at least three weeks apart. In each session, the physicians will evaluate each patient once with automated perfusion images and once without. We will use non-overlapping 95% confidence intervals and difference in agreement classification as criteria to suggest a difference between the Gwet AC1 statistics (κG). DISCUSSION: The results obtained from this study, combined with the clinical outcomes data of patients categorized through the two imaging techniques and a cost-effectiveness analysis, will offer a comprehensive evaluation of the clinical utility of perfusion CT neuroimaging. Should there be no significant disparity in the reliability of decisions made by clinicians using the two neuroimaging protocols, it may be necessary to revise existing recommendations regarding neuroimaging in the later time window to align with these findings.
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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 | 0.240 | 0.235 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".