P.121 Subgaleal versus subdural drain after minicraniotomy for chronic subdural haematoma
Bibliographic record
Abstract
Background: Subdural and subgaleal drains are equally effective after burrhole craniostomy for chronic subdural haematoma, however the optimal location of drains after minicraniotomy is not clear. As such we present the first study to assess this. Methods: Consecutive patients undergoing minicraniotomy for cSDH between 2019 and 2023 at a single institution were included. Subgaleal drains were placed exclusively by a single surgeon with the rest of the department utilising standard subdural drains. Cases were stratified by drain location. Primary outcomes included changes in functional status (Modified Rankin Score, mRS) at 3 months from preoperative baseline. Results: A total of 137 patients were included, of which 24.6% received subgaleal drains. Discharge home was higher in the subgaleal group compared to subdural group (79.4% vs 57.3%, p=0.02). Subgaleal drain location (p<0.0001) and better preoperative GCS (p=0.01) were predictors of improved 3 month mRS. Worse premorbid mRS (p=0.002), subdural drain (p=0.004), and decreased consciousness at presentation (p<0.002) were predictors of not being discharged home. Surgical recurrence was lower in the subgaleal group than the subdural group (2.9% vs 13.6%, p=0.12), but not statistically significant. Conclusions: Subgaleal drains are associated with shorter hospitalisation, greater chance of discharge home, and better functional outcomes than subdural drains.
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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 | 0.001 |
| 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) | 0.003 | 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".