Effect of non-neurological complications on the functional outcome of patients with ruptured intracranial aneurysms
Bibliographic record
Abstract
BACKGROUND: Aneurysmal subarachnoid hemorrhage (aSAH) is a life-threatening pathology associated with significant neurological and non-neurological complications. While the impact of neurological factors has been extensively studied, the impact of non-neurological complications remains underexplored. This study aimed to assess the effect of non-neurological complications on hospital stay and functional outcomes of patients with ruptured intracranial aneurysms (IAs). METHODS: A retrospective cohort study assessed patients with ruptured IAs treated within a neurovascular program of a tertiary hospital between October 2019 and September 2023. Inclusion criteria were: ≥18 years old, confirmed aSAH, and available follow-up information. The primary outcome corresponded to non-excellent functional outcomes at 6- and 12-month follow-ups. Secondary outcomes included length of in-hospital and intensive care unit (ICU) stay. Multivariate logistic regression models, adjusted for age, sex, and baseline World Federation of Neurosurgical Societies (WFNS) scores, were conducted. RESULTS: A total of 220 patients were included in this study, with a mean age of 56.66 ± 13.79 years; 74.5% were female. The most prevalent non-neurological complications were isolated fever (56.4%), arrhythmias (44.1%), and urinary tract infections (38.6%). Patients with poor neurological presentation had a higher prevalence of non-neurological complications. Pneumonia, pulmonary embolism, hyperglycemia, as well as fever were associated with higher odds of non-excellent functional outcomes (mRS 2-6) at 6- and 12-month follow-ups. CONCLUSIONS: Non-neurological complications significantly impact hospital stay and functional recovery in aSAH patients. Early diagnosis and intervention, as well as the implementation of comprehensive clinical algorithms, are crucial for improving long-term outcomes in patients with ruptured IAs.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".