Use of drains versus no drains after burr-hole evacuation of chronic subdural hematoma
Bibliographic record
Abstract
Background: Chronic subdural hematomas (cSDH) are a common neurosurgical problem with significant morbidity and mortality. Current treatment methods are variable. Post-operative subdural drain used in conjunction with burr-hole craniostomy may reduce recurrence. This study compared recurrence rates for cSDH between two surgical practices with and without use of post-operative subdural drain at the QEII Health Sciences Center. Methods: A retrospective chart review was conducted to compare recurrence rates between surgical patients treated with or without a post-operative drain between 1997- 2012. The primary endpoint was recurrence, defined as occurrence of symptoms due to hematoma confirmed by CT within six months of the original operation. Categorical frequencies were compared with chi square or Fisher’s exact test. Logistic regression was performed to identify risk factors for recurrence. Results: There were 85 patients (mean age 73 years; SD 13.0) who had burr-hole craniostomy. Age, cSDH volume, site, GCS, anticoagulation, drain, conservative treatment with steroids and perioperative steroids were not found to be independent predictors of recurrence. Recurrence occurred in 2 of 34 (5.9%) patients with drain, and in 7 of 51 (13.7%) without (p=0.305). There were insufficient data to compare mortality and complications. Conclusions: Use of post-operative subdural drain did not significantly alter the cSDH recurrence rate.
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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 | 0.000 | 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".