Risk Factors for Cognitive Impairment Following Angiographically Negative Subarachnoid Haemorrhage Around the Midbrain
Bibliographic record
Abstract
Objective: This study aimed to explore the risk factors for cognitive impairment caused by angiographically negative subarachnoid haemorrhage (SAH). Methods: This retrospective study employed a convenience sampling method to select patients with negative SAH in the midbrain who were admitted to the neurosurgery department of our hospital between September 2018 and September 2023. A total of 69 patients with angiographically negative SAH were enrolled and divided into the cognitive impairment group (n = 16) and the non-cognitive impairment group (n = 53). General demographic and clinical data were collected, and patients’ cognitive function was assessed using the Montreal Cognitive Assessment scale. The risk factors of the cognitive impairment caused by angiographically negative SAH were identified by logistic regression analysis. Results: The results of the univariate analysis showed that there were statistically significant differences ( p < 0.05) between the two groups of patients in terms of age, consciousness disorders, history of hypertension, ventricular haemorrhage, concurrent hydrocephalus, Glasgow Coma Scale score, Hunt–Hess grading (≥ 3) and Fisher grading (≥ 3). The logistic regression results showed that age ( p = 0.031), degree of consciousness impairment ( p = 0.023), Hunt–Hess grading ( p = 0.019), presence of hydrocephalus ( p = 0.002) and presence of ventricular haemorrhage ( p = 0.021) were independent risk factors for cognitive impairment after angiographically negative SAH ( p < 0.05). Conclusion: Age, degree of consciousness impairment, Hunt–Hess grade (≥ 3), concomitant ventricular haemorrhage and hydrocephalus are risk factors for cognitive function after angiographically negative SAH. Keywords: subarachnoid haemorrhage, computed tomography angiography, digital subtraction angiography, cognitive impairment
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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".