Predictors of good outcome in medium to large spontaneous supratentorial intracerebral haemorrhages
Bibliographic record
Abstract
OBJECTIVE: To determine potential predictors of good outcome in primary medium to large intracerebral haemorrhages (ICH) which could be useful for selecting patients for surgical procedures. METHODS: Subjects were 138 patients with spontaneous hemispheric ICH >20 ml. They were non-surgically treated and were admitted consecutively to 15 hospitals within the first 12 hours of symptom onset (mean (SD), 5.8 (3.1) hours). Haematoma volume was measured on computed tomography (CT) at admission. Stroke severity was assessed by the Canadian stroke scale (CSS). Good outcome was defined as modified Rankin score < or =2 at three months. RESULTS: At the end of the follow up period, 45 patients (32.6%) had good outcome. Baseline stroke severity, systolic and diastolic blood pressure, body temperature, and acute phase reaction biochemical markers (ESR, C-reactive protein, fibrinogen, neutrophil count) were significantly associated with good outcome in bivariate analyses. Of the initial CT scan variables, intraventricular contamination, deep location, mass effect, and greater ICH volume were related to poor outcome. On multiple logistic regression analysis, cortical location of bleeding (odds ratio 3.79 (95% confidence interval 1.2 to 12.01); p = 0.023), high CSS score (OR 2.3 (1.6 to 3.1); p<0.0001), and low fibrinogen concentrations (OR 0.92 (0.87 to 0.97); p = 0.001) were independent predictors of good outcome. These three factors correctly classified 85% of patients. CONCLUSIONS: Good outcome in medium to large ICH can be predicted on admission by three readily assessable factors (CSS score, ICH location, and fibrinogen levels). These predictors may be helpful in selecting patients for surgical treatment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".