Arachnoid granulations may be protective against the development of shunt dependent chronic hydrocephalus after aneurysm subarachnoid hemorrhage**
Bibliographic record
Abstract
Background and purpose Chronic hydrocephalus may develop as a sequela of aneurysmal subarachnoid hemorrhage, requiring long-term cerebrospinal fluid shunting. Several clinical predictors of chronic hydrocephalus and shunt dependence have been proposed. However, no anatomical predictors have been identified. Materials and methods A retrospective cohort study was performed including 61 patients with aneurysmal subarachnoid hemorrhage. Clinical characteristics were noted for each patient including presentation World Federation of Neurosurgical Societies grade, modified Fischer grade, aneurysm characteristics, requirement for acute and chronic cerebrospinal fluid diversion, and 3-month modified Rankin scale. CT images were evaluated to determine the Evans’ index and to enumerate the number of arachnoid granulations. Association between the clinical characteristics with ventriculoperitoneal shunt insertion and the 3-month modified Rankin scale were assessed. Results The initial Evans’ index was positively associated with mFisher grade and age, but not the number of arachnoid granulations. 16.4% patients required insertion of a ventriculoperitoneal shunt. The number of arachnoid granulations were a significant negative predictor of ventriculoperitoneal shunt insertion [OR: 0.251 (95% CI:0.073–0.862; p = 0.028)]. There was significant difference in the number of arachnoid granulations between those with and without ventriculoperitoneal shunt ( p = 0.002). No patient with greater than 4 arachnoid granulations required a ventriculoperitoneal shunt, irrespective of severity of initial grade. Conclusion Arachnoid granulations may be protective against the development of shunt dependent chronic hydrocephalus after aneurysmal subarachnoid hemorrhage. This is irrespective of presenting hemorrhage severity. This is a potentially novel radiologic biomarker and anatomic predictor of shunt dependence.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".