Interrater and Intrarater Measurement Reliability of Noncontrast Computed Tomography Predictors of Intracerebral Hemorrhage Expansion
Bibliographic record
Abstract
Background and Purpose- Early hematoma expansion after intracerebral hemorrhage is a potentially modifiable predictor of outcome and a promising therapeutic target. Radiological markers seen on noncontrast computed tomography can help predict hematoma expansion and risk stratify patients presenting with intracerebral hemorrhage. Our objective was to assess the interrater and intrarater reliability of 5 commonly reported noncontrast computed tomographic markers of hematoma expansion. Methods- Four readers independently reviewed images from 40 patients from 2 intracerebral hemorrhage imaging databases (PREDICT Collaboration [Predicting Haematoma Growth and Outcome in Intracerebral Haemorrhage Using Contrast Bolus CT] and Massachusetts General Hospital). Readers were blind to all demographic and outcome data and used accepted definitions to establish the presence or absence of intrahematoma hypodensities, blend sign, fluid level, irregular hematoma morphology, and heterogeneous hematoma density. We calculated interrater and intrarater agreement and stratified kappas for the 5 imaging markers. Results- Interrater agreement was excellent for all 5 markers, ranging from 94% to 98%. Interrater kappas ranged from 0.67 to 0.91 (the lowest for fluid level). Interrater agreement had a similar pattern, ranging from 89% to 93%, with Kappas ranging from 0.60 to 0.89. Conclusions- We show that 5 commonly used noncontrast computed tomographic imaging findings all have good-to-excellent interrater and intrarater reliabilities, with the best kappa for blend sign, hypodensities, and heterogeneity.
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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 | 0.044 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".