ASPECT Score and Its Application to Vasospasm in Aneurysmal Subarachnoid Haemorrhage: a Case–Control Study
Bibliographic record
Abstract
Delayed cerebral ischaemia (DCI) is a significant complication of aneurysmal subarachnoid haemorrhage (aSAH) and is strongly associated with poorer outcome. The Alberta Stroke Program Early Computer Tomography (ASPECT) score is an established scoring tool, used in acute ischaemic stroke, to quantify early ischaemic changes on CT head scans. We aim to identify if ASPECT scoring correlates with functional outcome in DCI following aSAH. Retrospective case-control study. Inclusion criteria: admission to the Department of Neurosurgery at Leeds Teaching Hospitals NHS Trust (a tertiary neurosurgical centre in the United Kingdom) between 2014 and 2018, with a diagnosis of anterior circulation aneurysmal subarachnoid haemorrhage; as confirmed by initial CT scan and subsequent CT angiography or catheter digital subtraction angiography. Cases were those who developed DCI (n = 43) and controls were randomly selected from those who did not develop DCI (n = 46) but otherwise met the same inclusion criteria. The primary outcome measure was Glasgow Outcome Score (GOS): assessed at discharge and 3 months. ASPECT scores were calculated from non-contrast CT head scans by three researchers blinded to each other and clinical outcome. Spearman's rank correlation was used to calculate correlation between ASPECT scores and GOS. ASPECT score positively correlated with GOS in the cases both at discharge (Spearman rho 0.436, p = 0.003) and at 3 months (Spearman rho 0.431, p = 0.004). When corrected for Fisher grading, the adjusted odds ratio of having a high GOS with a low ASPECT score at discharge was OR 0.74 (95% CI 0.61-0.94, p = 0.003), and 3 months OR 0.73 (95% CI 0.59-0.91, p = 0.005). ASPECT score significantly correlates with clinical outcome in DCI post aSAH, even after correcting for Fisher grade. ASPECT scoring may identify patients at risk of poor outcome following DCI and represents a quick and reliable tool that aids in clinical decision-making and prognostication.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".