Recurrent Chronic Subdural Hematoma After Burr-Hole Surgery and Postoperative Drainage: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Reported recurrence rates of chronic subdural hematoma treated by burr-hole surgery with postoperative drainage vary considerably in the literature. We performed a systematic review and meta-analysis to define the recurrence rate of burr-hole surgery with postoperative drainage. METHODS: PubMed and EMBASE were searched, and Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were followed. We used the Newcastle-Ottawa scale and Cochrane risk-of-bias tool for quality assessment of included studies and the random-effects model to calculate pooled incidence rates in R with the metaprop function if appropriate. RESULTS: The search yielded 2969 references; 709 were screened full text, and 189 met the inclusion criteria. In 174 studies (34 393 patients), the number of recurrences was reported as per patient and 15 studies (3078 hematomas) reported the number of recurrences per hematoma, for a pooled incidence of 11.2% (95% CI: 10.3-12.1; I 2 = 87.7%) and 11.0% (95% CI: 8.6-13.4; I 2 = 78.0%), respectively. The pooled incidence of 48 studies (15 298 patients) with the highest quality was 12.8% (95% CI 11.4-14.2; I 2 = 86.1%). Treatment-related mortality (56 patients) has a pooled incidence of 0.7% (95% CI 0.0-1.4; I 2 = 0.0%). CONCLUSION: The recurrence rate of chronic subdural hematoma treated by burr-hole surgery and postoperative drainage is 12.8%.
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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 | 0.015 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.045 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".