Abstract 40: Hematoma Expansion Shift Analysis to Assess Acute Intracerebral Hemorrhage Treatments
Bibliographic record
Abstract
Background and Purpose: In present-day intracerebral hemorrhage (ICH) trials, hematoma expansion (HE) is often analyzed as a binary variable - present or absent. However, dichotomizing intrinsically wide-ranging variables discards substantial information reflective of treatment effect and can reduce the ability to detect relationships between the exposure and outcome of interest. We explored the concept of a “HE shift analysis” as a novel analytic mode for ICH treatment trials using previously published clinical trial data. Methods: Using data from the Antihypertensive Treatment of Acute Cerebral Hemorrhage II (ATACH-2) trial, patients were stratified by treatment status (intensive vs. standard blood pressure lowering with IV nicardipine) and HE shift was evaluated by generating strata based on a) previously established HE definitions, b) quartiles of 24-hour total hematoma volume, and c) quartiles of 24-hour total hematoma volume (THV) change. The relationship between blood pressure treatment and hematoma expansion shift was explored using proportional odds models and adjusted Wilcoxon testing. Results: The primary analysis population included 863 patients. In both treatment groups, approximately one-third of patients exhibited no hematoma growth. Using a conventional threshold, significant intraparenchymal expansion (≥33%) was reduced by 5.8% in those assigned to the intensive therapy arm (aOR: 0.77 [95% CI: 0.60-0.99], Figure 1A). The stratified quartiles of 24-hour total hematoma volumes were associated with a 5% shift within the highest and lowest strata in the intensive arm, (aOR: 0.83 [0.66-1.05], Figure 1B). Intensive therapy was associated with a 5.2% reduction from > 3 mL to 0.5-3 mL (aOR: 0.86 [0.68-1.09], Figure 1C). Conclusions: A shift analysis model of HE provides additional insights into the biological effects of a given therapy and may be an alternate way to assess hemostatic agents in future hemorrhage trials.
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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 | 0.027 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".