Hemorrhagic transformation after stroke: inter‐ and intrarater agreement
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Hemorrhagic transformation (HT) is a complication of stroke that can occur spontaneously or after treatment. We aimed to assess the inter- and intrarater reliability of HT diagnosis. METHODS: Studies assessing the reliability of the European Cooperative Acute Stroke Study (ECASS) classification of HT or of the presence (yes/no) of HT were systematically reviewed. A total of 18 raters independently examined 30 post-thrombectomy computed tomography scans selected from the Aspiration versus STEnt-Retriever (ASTER) trial. They were asked whether there was HT (yes/no), what the ECASS classification of the particular scan (0/HI1/HI2/PH1/PH2) (HI indicates hemorrhagic infarctions and PH indicates parenchymal hematomas) was and whether they would prescribe an antiplatelet agent if it was otherwise indicated. Agreement was measured with Fleiss' and Cohen's κ statistics. RESULTS: The systematic review yielded four studies involving few (≤3) raters with heterogeneous results. In our 18-rater study, agreement for the presence of HT was moderate [κ = 0.55; 95% confidence interval (CI), 0.41-0.68]. Agreement for ECASS classification was only fair for all five categories, but agreement improved to substantial (κ = 0.72; 95% CI, 0.69-0.75) after dichotomizing the ECASS classification into 0/HI1/HI2/PH1 versus PH2. The inter-rater agreement for the decision to reintroduce antiplatelet therapy was moderate for all raters, but substantial among vascular neurologists (κ = 0.70; 95% CI, 0.57-0.84). CONCLUSION: The ECASS classification may involve too many categories and the diagnosis of HT may not be easily replicable, except in the presence of a large parenchymal hematoma.
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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 | 0.280 | 0.383 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.007 |
| 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".