Hematoma Expansion Shift Analysis to Assess Acute Intracerebral Hemorrhage Treatments
Bibliographic record
Abstract
OBJECTIVE: Hematoma expansion (HE) is commonly analyzed as a dichotomous outcome in intracerebral hemorrhage (ICH) trials. In this proof-of-concept study, we propose an HE shift analysis model as a method to improve the evaluation of candidate ICH therapies. METHODS: Using data from the Antihypertensive Treatment of Acute Cerebral Hemorrhage II (ATACH-2) trial, we performed HE shift analysis in response to intensive blood pressure lowering by generating polychotomous strata based on previously established HE definitions, percentile/absolute quartiles of hematoma volume change, and quartiles of 24-hour follow-up hematoma volumes. The relationship between blood pressure treatment and HE shift was explored with proportional odds models. RESULTS: The primary analysis population included 863 patients. In both treatment groups, approximately one-third of patients exhibited no HE. With the use of a trichotomous HE stratification, the highest strata of ≥33% revealed a 5.8% reduction in hematoma growth for those randomized to intensive therapy (adjusted odds ratio [aOR] 0.77, 95% confidence interval [CI] 0.60-0.99). Using percentile quartiles of hematoma volume change, we observed a favorable shift to reduce growth in patients treated with intensive therapy (aOR 0.73, 95% CI 0.57-0.93). Similarly, in a tetrachotomous analysis of 24-hour follow-up hematoma volumes, shifts in the highest stratum (>21.9 mL) were most notable. CONCLUSIONS: Our findings suggest that intensive blood pressure reduction may preferentially mitigate growth in patients at risk of high volume HE. A shift analysis model of HE provides additional insights into the biological effects of a given therapy and may be an additional way to assess hemostatic agents in future studies. TRIAL REGISTRATION INFORMATION: ClinicalTrials.gov Identifier:NCT01176565.
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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 | 0.030 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".