P.082 Traumatic inter hemispheric subdural hematomas – clinical presentation, management and outcome
Bibliographic record
Abstract
Background: There is currently little data on the incidence, clinical outcome and management of traumatic interhemispheric subdural hematomas (IHSDHs). Methods: All patients admitted with an acute subdural hematoma (SDH) over a 5-year period at a Level I trauma center were included. A detailed review of all cases of large IHSDH (≥7 mm) was performed to document clinical presentation, management and outcomes. Results: Of 1182 patients with acute subdural hematomas (SDHs), 420 had IHSDHs (24%), and 50 were large IHSDHs. For patients with large IHSDH, the average age was 76 years (±11) and 44% were female. The average GCS was 12 on presentation (±4), and the average GOSE was 4 (±2). 66% of patients had associated cranial/ intracranial injuries (fracture, subarachnoid/epidural/SDH) and 26% required operations for acute convexity SDH. Three patients required operations for their IHSDH by inter hemispheric approach. By 10 weeks, 82% had a complete resolution of the IHSDHs. Conclusions: IHSDHs are often referred to as rare entities. Our results show they are common. Conservative management is often appropriate to manage even large IHSDHs, as most resolve spontaneously. This study will help document the occurrence of falx syndrome, as well as the management and outcomes of larger IHSDHs.
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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 | 0.001 |
| 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) | 0.008 | 0.003 |
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".