P.119 Clinical outcome and recurrence rate of chronic subdural hematoma after surgical drainage: a retrospective study
Bibliographic record
Abstract
Background: Chronic subdural hematoma (CSDH) is of the most encountered neurosurgical cases, predominantly in older individuals. Surgical drainage remains the mainstay, yet is challenged by variable recurrence rates. Less invasive methods of embolization of the middle meningeal artery (EMMA) could reduce the recurrence rates. Before adopting a newer treatment (EMMA), it is prudent to establish the outcomes from surgical drainage. The purpose of this study is to assess the clinical outcome and recurrence risk in surgically treated CSDH patients. Methods: A retrospective search of our surgical database was done to identify CSDH patients undergoing surgical drainage in 2019-2020. Demographic and clinical details were collected through chart review and a qualitative statistical analysis was performed. Results: A total of 136 patients (mean age-68 years; range-21-100 years; Male-105) with CSDH underwent surgical drainage with repeat surgery in 11.8%(n=16). Periprocedural mortality and morbidity were 8.8%(n=12) and 20.6%(n=28), respectively. No radiological follow-up was seen in 30(22%) of patients. Of those with follow-up, recurrence was seen in 21.7%(n=23). Mean hospital stay was 9.64 days. Conclusions: Our retrospective study showed periprocedural morbidity (20.6%) and mortality (8.8%) with a 21.7% risk of recurrence. This is likely due to older patients but is in keeping with what is reported in the literature.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".