P.199 Management of ruptured arachnoid cysts with hemorrhage: a bayesian network analysis
Bibliographic record
Abstract
Background: Arachnoid cysts are fluid collections within the arachnoid membrane. Although rare, ruptured or hemorrhagic arachnoid cysts pose significant clinical challenges and management controversies. The present study analyzes factors influencing their treatment decision using Bayesian network models. Methods: PubMed and EMBASE databases were searched to identify reports of ruptured arachnoid cysts with patient-level data. Demographic, clinical, imaging and treatment data were extracted to develop Tree-augmented naïve Bayes (TAN) classifiers for analyzing the factors influencing decision of surgery and type of surgery. A web application was developed to explore the networks. Results: Middle cranial fossa cysts were most common (95%) along with a male predominance (M:F ratio 4.29:1). Headache and vision changes were the most common symptoms and >50% had a history of head injury. Surgery was performed in 89.8% of cases with craniotomy being the most common surgical procedure. Key factors influencing the decision of surgery were cyst location, hemorrhage type, age group, and Galassi classification, while type of surgery was also influenced by head trauma, seizures, and macrocrania. Conclusions: Bayesian network analysis demonstrates that decision of surgical treatment of a ruptured arachnoid cyst is dependent on multiple interdependent factors and should be individualized to match the presentation with the surgical modality.
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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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.009 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.006 | 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; both teacher heads agree on what is shown here.
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".