Defining hematoma expansion in intracerebral hemorrhage
Bibliographic record
Abstract
BACKGROUND: Hematoma expansion (HE) is a surrogate marker in intracerebral hemorrhage (ICH) trials. However, the amount of HE necessary to produce poor outcomes in an individual is unclear; there is no agreement on a clinically meaningful definition of HE. We compared commonly used definitions of HE in their ability to predict poor outcome as defined by various cutpoints on the modified Rankin Scale (mRS). METHODS: In this cohort study, we analyzed 531 patients with ICH from the Virtual International Stroke Trials Archive. Primary outcome was mRS at 90 days, dichotomized into 0-3 vs 4-6. Secondary outcomes included other mRS cutpoints and mRS "shift analysis." Sensitivity, specificity, and predictive values for commonly used HE definitions were calculated. RESULTS: Between 13% and 32% of patients met the commonly used HE definitions. All definitions independently predicted poor outcome; positive predictive values increased with higher growth cutoffs but at the expense of lower sensitivities. All HE definitions showed higher specificity than sensitivity. Absolute growth cutoffs were more predictive than relative cutoffs when mRS 5-6 or 6 was defined as "poor outcome." CONCLUSION: HE robustly predicts poor outcome regardless of the growth definition or the outcome definition. The highest positive predictive values are obtained when using an absolute growth definition to predict more severe outcomes. Given that only a minority of patients may have clinically relevant HE, hemostatic ICH trials may need to enroll a large number of patients, or select for a population that is more likely to have HE.
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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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".