Molecular signatures of brain injury after intracerebral hemorrhage
Bibliographic record
Abstract
BACKGROUND: The mechanisms of cellular death in the tissue surrounding an intracerebral hemorrhage (ICH) are not defined. OBJECTIVE: To investigate the relationship of markers of excitotoxicity and inflammation to brain injury after ICH. METHODS: A total of 124 consecutive patients with spontaneous ICH admitted within 24 hours of stroke onset were prospectively investigated. The volumes of the initial ICH, peripheral edema on days 3 to 4, and the residual cavity at 3 months were measured on CT scan. Glutamate, cytokines, and adhesion molecules were measured in blood samples obtained on admission. Stroke severity and neurologic outcome were evaluated with the Canadian Stroke Scale. RESULTS: Poor neurologic outcome at 3 months (Canadian Stroke Scale < 7) was observed in 53 patients (43%). Stroke severity and glutamate concentrations (by each increment of 10 micromol/L, odds ratio 1.23; 95% CI 1.09 to 1.41), but not the initial volume of ICH, were independent predictors of poor outcome. In the multiple linear regression analyses, tumor necrosis factor-alpha concentration was correlated (r = 0.83, p < 0.0001) with the volume of perihematoma edema, and glutamate concentrations were correlated (r = 0.78, p < 0.0001) with the volume of the residual cavity. These same results were observed when lobar (n = 58) and deep (n = 66) ICH were analyzed separately. CONCLUSIONS: High plasma levels of proinflammatory molecules within 24 hours of intracerebral hemorrhage onset are correlated with the magnitude of the subsequent perihematoma brain edema, whereas poor neurologic outcome and the volume of the residual cavity are related to increased plasma glutamate concentrations.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".