Clinical Characterization of Traumatic Acute Interhemispheric Subdural Hematoma
Bibliographic record
Abstract
OBJECT: Interhemispheric subdural hematomas (IHSDHs) are thought to be rare. Surgical management of these lesions presents a challenge as they are in close proximity to the sagittal sinus and bridging veins. IHSDHs are poorly characterized clinically and their exact incidence is unknown. There are also no clear guidelines for the management of IHSDH. METHODS: This is a retrospective review of all admitted patients with a diagnosis of traumatic brain injury over a 4-year period at a Level I trauma centre. Clinical characteristics of all patients with subdural hematoma (SDH) and IHSDH were collected. RESULTS: Of 2165 admissions, 1182 patients had acute traumatic SDHs, 420 patients had IHSDHs (1.9% of admissions and 35.5% of SDH), 35 (8.3% of IHSDH) were ≥8 mm in width. IHSDH was isolated in 16 (3.8%) of the cases. Average age was 61.7 ± 21.5 years for all IHSDHs and 77.1 ± 10.4 for large IHSDH (p < 0.001). For large IHSDH, a transient loss of consciousness (LOC) occurred in 51.5% of individuals, post-traumatic amnesia (PTA) in 47.8% of cases, and motor weakness in 37.9% of patients. Five of the large IHSDH patients presented with motor deficits directly related to the IHSDH, and weakness resolved in four of these five individuals. None were treated surgically. Progression of IHSDH width occurred in one patient. CONCLUSION: IHSDHs are often referred to as rare entities. Our results show they are common. Conservative management is appropriate to manage most IHSDHs, as most resolve spontaneously, and their symptoms resolve as well.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".