P.124 Delayed cerebral ischemia and cognitive outcomes after aneurysmal subarachnoid hemorrhage: an exploratory analysis
Bibliographic record
Abstract
Background: Neuropsychological outcomes are an important component of the morbidity after aneurysmal subarachnoid hemorrhage (aSAH). Data on the relationship between delayed cereberal ischemia (DCI) and neuropsychological outcomes remains sparse. We herein assess the relationship between DCI and neuropsychological outcomes, as measured by the Montreal Cognitive Assessment score (MoCA) at 90 days in patients with aSAH. Methods: We performed a post-hoc analysis of the Nimodipine Microparticles to Enhance Recovery While Reducing Toxicity After Subarachnoid Hemorrhage (NEWTON-2) clinical trial. Patients were grouped based on whether they developed delayed cerebral ischemia. We assessed the relationship between MoCA scores and DCI with Student’s t-test and regression modeling. Age, sex, history of hypertension, and WFNS grade were included as covariates in the model. Results: Two-hundred and fifteen patients were included in our analysis. Mean MoCA score at 90 days in our population was 22. Mean MoCA scores were significantly lower in patients who developed DCI compared to those who did not (23.7 vs 18.4, p<0.001). Age, WFNS grade, and development of DCI were independently associated with MoCA scores in the regression model (p < 0.05). Conclusions: DCI is a predictor of decreased neuropsychological outcomes in aSAH survivors and may contribute to the morbidity burden in this population.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".