Third Ventricle Volume Predicts Functional Outcome in Chronic Subdural Hematoma
Bibliographic record
Abstract
OBJECTIVES: There is a lack of evidence demonstrating the utility of computed tomography (CT) to predict chronic subdural hematoma (CSDH) clinical outcomes. We aim to analyze the role of tomographic volumetric analysis in patients with CSDH. METHODS: We performed a retrospective study of patients undergoing burr-hole craniostomy (BHC) for CSDH over five years at a tertiary care center. Degree of midline shift, radiographic density, subdural hematoma volume, acute blood volume, and third ventricle (3VV) and fourth ventricle (4VV) volume were estimated using semiautomatic segmentation of preoperative CT. Postoperative functional outcome was measured by two endpoints: National Institute of Health Stroke Scale (NIHSS) at discharge and short-term modified Rankin Scale (mRS) at 6-week follow-up. Univariate and multivariate analyses were performed using nonparametric tests. Discriminative capacity and optimal thresholds of independent variables were calculated by means of receiving-operative curves (ROC). RESULTS: A total of 79 patients were included for analysis with a median age of 78.5 years. Greater preoperative 3VV independently correlated with poor discharge NIHSS (p = .01) and short-term mRS (p = .03). A cutoff value of 0.545 mL demonstrated the highest sensitivity (77.1%) and specificity (88.8%) with an odds ratio for an mRS functional dependence of 9.29 (p = .001). CONCLUSIONS: Greater preoperative tomographic 3VV independently prognosticates poor discharge NIHSS and 6-week mRS. A threshold 3VV of 0.545 mL can be used to identify patients at higher risk of being dependent at first protocolized follow-up.
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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 | 0.000 | 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) | 0.001 | 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".