Prognostic Factors for Outcome in Patients With Aneurysmal Subarachnoid Hemorrhage
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The purpose of this study was to describe prognostic factors for outcome in a large series of patients undergoing neurosurgical clipping of aneurysms after subarachnoid hemorrhage (SAH). METHODS: Data were analyzed from 3567 patients with aneurysmal SAH enrolled in 4 randomized clinical trials between 1991 and 1997. The primary outcome measure was the Glasgow outcome scale 3 months after SAH. Multivariable logistic regression with backwards selection and Cox proportional hazards regression models were derived to define independent predictors of unfavorable outcome. RESULTS: In multivariable analysis, unfavorable outcome was associated with increasing age, worsening neurological grade, ruptured posterior circulation aneurysm, larger aneurysm size, more SAH on admission computed tomography, intracerebral hematoma or intraventricular hemorrhage, elevated systolic blood pressure on admission, and previous diagnosis of hypertension, myocardial infarction, liver disease, or SAH. Variables present during hospitalization associated with poor outcome were temperature >38 degrees C 8 days after SAH, use of anticonvulsants, symptomatic vasospasm, and cerebral infarction. Use of prophylactic or therapeutic hypervolemia or prophylactic-induced hypertension were associated with a lower risk of unfavorable outcome. Time from admission to surgery was significant in some models. Factors that contributed most to variation in outcome, in descending order of importance, were cerebral infarction, neurological grade, age, temperature on day 8, intraventricular hemorrhage, vasospasm, SAH, intracerebral hematoma, and history of hypertension. CONCLUSIONS: Although most prognostic factors for outcome after SAH are present on admission and are not modifiable, a substantial contribution to outcome is made by factors developing after admission and which may be more easily influenced by treatment.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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 it