Treatment modality for aneurysmal subarachnoid hemorrhage and risk of shunt dependent hydrocephalus and mortality: population based study
Bibliographic record
Abstract
BACKGROUND: Hydrocephalus is a significant contributor to morbidity following aneurysmal subarachnoid hemorrhage (aSAH). We aimed to investigate the association between primary treatment modality and the incidence of hydrocephalus requiring CSF diversion, using a target trial approach for causal inference. METHODS: This cohort study used US administrative health claims data (Clinformatics Data Mart) and was conducted among aSAH patients undergoing primary treatment with either clipping or coiling, from January 1, 2004, to February 28, 2023. The primary outcome was hydrocephalus requiring CSF diversion surgery while the secondary outcome was mortality. Multivariable regression and 1:1 propensity score (PS) matching were used for confounder control. Crude and adjusted hazard ratios (HRs) with 95% CIs were calculated. RESULTS: A total of 5816 patients (mean age 59 years; 72% women) undergoing clipping (n=1794) or coiling (n=4022) were included in the primary cohort. The 1:1 PS matched cohort had 1794 participants per arm. Clipping demonstrated higher hazards of shunt dependent hydrocephalus compared with coiling in both the multivariable Fine-Gray model (HR 1.39, 95% CI 1.19 to 1.62) and the PS matched cohorts (HR 1.39, 95% CI 1.16 to 1.66). Mortality analysis favored clipping in the crude analysis (HR 0.78, 95% CI 0.69 to 0.88) but leaned toward coiling after confounder adjustment (HR 1.13, 95% CI 1.00 to 1.29 in the multivariable model; HR 1.11, 95% CI 0.95 to 1.29 in the PS matched cohort). CONCLUSION: These findings suggest that coiling is associated with reduced hazards of shunt dependent hydrocephalus following aSAH compared with clipping, and provide valuable insights for shared decision making among clinicians and patients, in the context of conflicting evidence from smaller observational studies.
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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".