New and expanding ventricular hemorrhage predicts poor outcome in acute intracerebral hemorrhage
Bibliographic record
Abstract
OBJECTIVE: To describe the relationship between intraventricular hemorrhage (IVH) expansion and long-term outcome and to use this relationship to select and validate clinically relevant thresholds of IVH expansion in 2 separate intracerebral hemorrhage (ICH) populations. METHODS: We used fractional polynomial analysis to test linear and nonlinear models of 24-hour IVH volume change and clinical outcome with data from the Predicting Hematoma Growth and Outcome in Intracerebral Hemorrhage Using Contrast Bolus CT (PREDICT)-ICH study. The primary outcome was poor clinical outcome (modified Rankin Scale [mRS] score 4-6) at 90 days. We derived dichotomous thresholds from the selected model and calculated diagnostic accuracy measures. We validated all thresholds in an independent single-center ICH cohort (Massachusetts General Hospital). RESULTS: Of the 256 patients from PREDICT, 127 (49.6%) had an mRS score of 4 to 6. Twenty-four-hour IVH volume change and poor outcome fit a nonlinear relationship, in which minimal increases in IVH were associated with a high probability of an mRS score of 4 to 6. IVH expansion ≥1 mL (n = 53, sensitivity 33%, specificity 92%, adjusted odds ratio [aOR] 2.68, 95% confidence interval [CI] 1.11-6.46) and development of any new IVH (n = 74, sensitivity 43%, specificity 85%, aOR 2.53, 95% CI 1.22-5.26) strongly predicted poor outcome at 90 days. The dichotomous thresholds reproduced well in a validation cohort of 169 patients. CONCLUSION: IVH expansion as small as 1 mL or any new IVH is strongly predictive of poor outcome. These findings may assist clinicians with bedside prognostication and could be incorporated into definitions of hematoma expansion to inform future ICH treatment trials.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".