Clinical prediction of delayed cerebral ischemia in aneurysmal subarachnoid hemorrhage
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to derive a clinically applicable decision rule using clinical, radiological, and laboratory data to predict the development of delayed cerebral ischemia (DCI) in aneurysmal subarachnoid hemorrhage (aSAH) patients. METHODS: Patients presenting over a consecutive 9-year period with subarachnoid hemorrhage (SAH) and at least 1 angiographically evident aneurysm were included. Variables significantly associated with DCI in univariate analysis underwent multivariable logistic regression. Using the beta coefficients, points were assigned to each predictor to establish a scoring system with estimated risks. DCI was defined as neurological deterioration attributable to arterial narrowing detected by transcranial Doppler ultrasonography, CT angiography, MR angiography, or catheter angiography, after exclusion of competing diagnoses. RESULTS: Of 463 patients, 58% experienced angiographic vasospasm with an overall DCI incidence of 21%. Age, modified Fisher grade, and ruptured aneurysm location were significantly associated with DCI. This combination of predictors had a greater area under the receiver operating characteristic curve than the modified Fisher grade alone (0.73 [95% CI 0.67-0.78] vs 0.66 [95% CI 0.60-0.71]). Patients 70 years or older with modified Fisher grade 0 or 1 SAH and a posterior circulation aneurysm had the lowest risk of DCI at 1.2% (0 points). The highest estimated risk was 38% (17 points) in patients 40-59 years old with modified Fisher grade 4 SAH following rupture of an anterior circulation aneurysm. CONCLUSIONS: Among patients presenting with aSAH, this score-based clinical prediction tool exhibits increased accuracy over the modified Fisher grade alone and may serve as a useful tool to individualize DCI risk.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".