Patient Age and Vasospasm After Subarachnoid Hemorrhage
Bibliographic record
Abstract
BACKGROUND: Subarachnoid hemorrhage (SAH) from a ruptured intracranial aneurysm is a devastating disease with high mortality and morbidity. The incidence of SAH increases with advancing age. OBJECTIVE: To determine whether age is an independent predictor of angiographic vasospasm, delayed ischemic neurological deficits (DINDs), or abnormal transcranial Doppler (TCD) measurements in patients with aneurysmal subarachnoid hemorrhage. METHODS: Data from CONSCIOUS-1 (Clazosentan to Overcome Neurological Ischemia and Infarct Occurring After Subarachnoid Hemorrhage study), a dose-finding study of clazosentan, were used. Data on angiographic vasospasm, DINDs, and TCD abnormalities were prospectively recorded as well as baseline characteristics and treatment data. Patient age was considered in 3 ways: as a continuous variable, dichotomized at age 65 years, and categorized by decade. Age was investigated as the main variable, whereas other possible confounding variables were adjusted for in the multiple logistic regression modeling with each of 3 dichotomized vasospasm outcome measures, presence or absence of angiographic vasospasm, DINDs, and TCD abnormalities as the dependent variable. RESULTS: The proportions of patients with angiographic vasospasm, DINDs, and TCD abnormalities were 45%, 19%, and 81%, respectively. Age, whether considered as a continuous, dichotomous, or a categorical variable, was not significantly associated with angiographic vasospasm, DINDs, or abnormal TCD measurements. CONCLUSION: Age does not seem to be a significant predictor for cerebral vasospasm after subarachnoid hemorrhage.
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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 | 0.000 | 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 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".