Trauma Mechanisms and Surgical Outcomes in the Elderly Patient with Chronic Subdural Hematoma
Bibliographic record
Abstract
Background Chronic subdural hematoma is the preeminent neurosurgical condition in the older population. This retrospective single-centre study focuses on outcome after surgery of chronic subdural hematoma in patients over 70 years. Methods Patients treated at a single neurosurgical referral centre between 2010 and 2014 were screened. Included patients were assessed for comorbid conditions, lifestyle factors, and outcomes including recurrence, mortality, and postoperative complications. Results A total of 511 patients (70–97 yrs) were identified. 50.7% of patients were treated with anticoagulants and/or antiplatelet therapy. A known probable cause for the hematoma was found in 68.1% of patient’s histories. Mortality rate was 3.1% and recurrence was seen in 49 patients (9.6%). Postoperative complications were more common in patients with excessive use of alcohol (p value = .02). Neurological function was improved in 78.1% of patients after the initial surgery. A strategy of delayed contralateral surgery in bilateral hematomas showed low rates of recurrence. Conclusion Fall injuries are the most common underlying trauma mechanism in the elderly with chronic subdural hematoma. Recurrence is not more common in the elderly patient group compared to the general population. Excessive alcohol use is a risk factor for post-operative complications.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".