Abstract 194: Cognitive Outcome in Acute Simvastatin Treatment for Aneurysmal Subarachnoid Hemorrhage
Bibliographic record
Abstract
Objectives: Experimental evidence has indicated the benefit of simvastatin in the treatment of subarachnoid haemorrhage (SAH). Recently, acute simvastatin treatment was not shown to be beneficial in neurological outcome using modified Rankin Scale. Cognitive function is another important dimension of outcome assessment and yet had not been investigated in statin studies for aneurysmal subarachnoid hemorrhage. We therefore explored whether acute simvastatin treatment would improve cognitive outcomes. Methods: The study recruited SAH patients with acute simvastatin treatment enrolled in a randomized controlled double-blinded clinical trial (ClinicalTrials.gov Identifier: NCT01038193). A control cohort of SAH patients without simvastatin treatment was identified with propensity score matching of age and admission grade. Primary outcome measure was Montreal Cognitive Assessment (MoCA). Secondary outcome measures were delayed ischaemic deficit (DID), delayed cerebral infarction, modified Rankin Scale (mRS), and Mini-Mental State Examination( MMSE). Results: Fifty-one SAH patients with acute simvastatin treatment and 51 SAH patients without simvastatin treatment were recruited for analysis. At 3 months, there were no differences in MoCA scores (MoCA: 21+/-6 vs. 21+/-5, p=0.772). MoCA-assessed cognitive impairment (MoCA<26) was not different (75% vs. 80%, OR 0.7, 95%CI 0.3 to 1.8, p=0.477). There were also no differences in DID, delayed cerebral infarction, favorable mRS outcome, and MMSE scores, and MMSE-assessed cognitive impairment between both groups. Conclusions: The current study does not support that acute simvastatin treatment improves cognitive outcome after aneurysmal subarachnoid hemorrhage.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.003 | 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".