Effect of choice of treatment modality on the incidence of shunt-dependent hydrocephalus after aneurysmal subarachnoid hemorrhage
Bibliographic record
Abstract
OBJECTIVE: Shunt-dependent hydrocephalus (SDHC) may arise after aneurysmal subarachnoid hemorrhage (aSAH) as CSF resorptive mechanisms are disrupted. Using propensity score analysis, the authors aimed to investigate which treatment modality, surgical clipping or endovascular treatment, is superior in reducing rates of SDHC after aSAH. METHODS: The authors' multicenter SAH database, comprising 3 stroke centers affiliated with Kyoto University, Japan, was used to identify patients treated between January 2009 and July 2016. Univariate and multivariate analyses were performed to characterize risk factors for SDHC after aSAH. A propensity score model was generated for both treatment groups, incorporating relevant patient covariates to detect any superiority for prevention of SDHC after aSAH. RESULTS: A total of 566 patients were enrolled in this study. SDHC developed in 127 patients (22%). On multivariate analysis, age older than 53 years, the presence of intraventricular hematoma, and surgical clipping as opposed to endovascular coiling were independently associated with SDHC after aSAH. After propensity score matching, 136 patients treated with surgical clipping and 136 with endovascular treatment were matched. Propensity score-matched cohorts exhibited a significantly lower incidence of SDHC after endovascular treatment than after surgical clipping (16% vs 30%, p = 0.009; OR 2.2, 95% CI 1.2-4.2). SDHC was independently associated with poor neurological outcomes (modified Rankin Scale score 3-6) at discharge (OR 4.3, 95% CI 2.6-7.3; p < 0.001). CONCLUSIONS: SDHC after aSAH occurred significantly more frequently in patients who underwent surgical clipping. Strategies for treatment of ruptured aneurysms should be used to mitigate SDHC and minimize poor outcomes.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".