Determinants of Intracerebral Hemorrhage Growth
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: We report an exploratory analysis from a randomized study of recombinant activated factor VII (rFVIIa) in patients with intracerebral hemorrhage (ICH) examining potential factors associated with hemorrhage growth. METHODS: We explored the relationship between 5 different measures of change in hemorrhage volume between baseline and 24-hour CTs (absolute and percent change in ICH volume, ICH growth-categoric [no growth if change <33% and <12.5 mL], absolute and percent change in ICH plus intraventricular hemorrhage [IVH] volume) and 31 demographic, clinical, imaging, historic, and baseline laboratory variables. Variables with a probability value of < or =0.10 were included in the final multivariable models. RESULTS: Treatment with rFVIIa and a longer time-from-onset-to-baseline CT were related to a decrease in hemorrhage growth in all 5 models. ICH volume on baseline CT was consistently associated with ICH growth in the various models. Other variables significantly related to growth of ICH or ICH+IVH in at least 1 of the 5 models include serum glucose (increased levels associated with increased growth), body mass index (heavier people have less growth), prior use of antiplatelet agent (prior use associated with increased growth), serum cholesterol (higher level associated with less hemorrhage growth), and serum creatinine (higher level associated with more hemorrhage growth). CONCLUSIONS: Our exploratory analyses confirm that treatment with rFVIIa limits ICH growth in subjects with spontaneous ICH who met the criteria for this study. Most hematoma growth occurs early after onset of ICH. Larger hematomas on the baseline CT were associated with increased absolute ICH growth. The relationship of other factors to hemorrhage growth warrants further study.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".