P.116 Predicting cerebral vasospasm following aneurysmal subarachnoid hemorrhage is still an imperfect science
Bibliographic record
Abstract
Background: Cerebral vasospasm is a leading cause of neurological disability following aneurysmal subarachnoid hemorrhage (aSAH). Clinical features associated with vasospasm development include blood burden on CT, neurological status, age and aneurysm location. Early cerebral CT perfusion (CTP) scanning in aSAH may be an independent predictor of vasospasm and/or delayed cerebral ischemia (DCI). Methods: Forty-one patients with aSAH were prospectively enrolled. Baseline data collected included WFNS grade, loss of consciousness at ictus, and modified Fisher grade. CTP was obtained at baseline and on day 6 post SAH. Cerebral blood volume, cerebral blood flow and mean transit time were measured. DCI was confirmed by a combination of clinical assessments, non-contrast CT and CTP. Radiological vasospasm was assessed with CT angiography. Results: Despite 80% of patients having a modified Fisher grade 3 or 4 aSAH, one-third presenting with ictal LOC and half having anterior communicating artery aneurysms, only one patient developed clinical evidence of vasospasm/DCI. Two others had asymptomatic radiological vasospasm. CTP parameters did not differ between groups defined by clinical predictors. Conclusions: In an unexpected finding, clinical and radiological vasospasm were very uncommon in this cohort. Clinical predictive variables correlated poorly with development of vasospasm. CTP may help refine the model but further work is needed.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".