Interobserver agreement in perfusion computed tomography evaluation in acute ischaemic stroke.
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: A growing body of evidence suggests that semiquantitative assessment of perfusion computed tomography (PCT) may improve evaluation of acute ischaemic stroke patients and provide some prognostic values. The Alberta Stroke Program Early CT Score (ASPECTS) is one of the tools quantifying ischaemic changes on CT scans. While introducing PCT for routine evaluation of patients with clinical suspicion of acute stroke in our Neurology Department, we aimed to investigate the agreement in analysis of non-contrast CT (NCCT) and PCT using ASPECTS between neuroradiologists and stroke neurologists. MATERIAL AND METHODS: We analyzed the data of 34 patients with hemispheric ischaemic stroke, in whom NCCT and PCT were performed within 12 hours after stroke onset. Two pairs of reviewers independently assessed NCCT and PCT [colour-coded maps of cerebral blood flow (CBF), cerebral blood volume (CBV), and time-to-peak (TTP)] using ASPECTS. Based on the literature data, we dichotomized the score. The chosen cut-off points were: 6 vs. <6, 7 vs. <7, and 8 vs. <8. The agreement was determined using kappa statistics. RESULTS: A better agreement was achieved for PCT maps compared with NCCT scans and when the cut-off point was 7 vs. <7. The results were as follows: for NCCT fair agreement (k=0.27), for CBF and for CBV moderate agreement (k=0.46 and k=0.57, respectively), for TTP substantial agreement (k=0.77). CONCLUSIONS: A good agreement in semiquantitative assessment of PCT using ASPECTS indicates that it is a reliable tool to analyze acute ischaemic stroke patients and superior to NCCT.
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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 | 0.046 | 0.111 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".