Predicting Recurrence after Chronic Subdural Haematoma Drainage
Bibliographic record
Abstract
BACKGROUND: Recurrence of chronic subdural haematomas (CSDHs) after surgical drainage is a significant problem with rates up to 20%. This study focuses on determining factors predictive of haematoma recurrence and presents a scoring system stratifying recurrence risk for individual patients. METHODS: Between the years 2005 and 2009, 331 consecutive patients with CSDHs treated with surgery were included in this study. Univariate and multivariate analyses were performed searching for risk factors of increased post-operative haematoma volume and haematoma recurrence requiring repeat drainage. RESULTS: We found a 12% reoperation rate. CSDH septation (seen on computed tomogram scan) was found to be an independent risk factor for recurrence requiring reoperation (p=0.04). Larger post-operative subdural haematoma volume was also significantly associated with requiring a second drainage procedure (p<0.001). Independent risk factors of larger post-operative haematoma volume included septations within a CSDH (p<0.01), increased pre-operative haematoma volume (p<0.01), and a greater amount of parenchymal atrophy (p=0.04). A simple scoring system for quantifying recurrence risk was created and validated based on patient age (< or ≥ 80 years), haematoma volume (< or ≥ 160 cc), and presence of septations within the subdural collection (yes or no). CONCLUSION: Septations within CSDHs are associated with larger post-operative residual haematoma collections requiring repeat drainage. When septations are clearly visible within a CSDH, craniotomy might be more suitable as a primary procedure as it allows greater access to a septated subdural collection. Our proposed scoring system combining haematoma volume, age, and presence of septations might be useful in identifying patients at higher risk for recurrence.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".