Clinical outcome prediction in aneurysmal subarachnoid hemorrhage - Alterations in brain-body interface
Bibliographic record
Abstract
BACKGROUND: Brain-body associations are essential in influencing outcome in patients with ruptured brain aneurysms. Thus far, there is scarce literature on such important relationships. METHODS: The multicenter Tirilazad database (3551 patients) was used to create this clinical outcome prediction model in order to elucidate significant brain-body associations. Traditional binary logistic regression models were used. RESULTS: Binary logistic regression main effects model included four statistically significant single prognostic variables, namely, neurological grade, age, stroke, and time to surgery. Logistic regression models demonstrated the significance of hypertension and liver disease in development of brain swelling, as well as the negative consequences of seizures in patients with a history of myocardial infarction and post-admission fever worsening neurological outcome. CONCLUSIONS: Using the aforementioned results generated from binary logistic regression models, we can identify potential patients who are in the high risk group of neurological deterioration. Specific therapies can be tailored to prevent these detriments, including treatment of hypertension, seizures, early detection and treatment of myocardial infarction, and prevention of hepatic encephalopathy.
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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) | 0.001 | 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 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".