Spontaneous spinal epidural hematoma management: a case series and literature review
Bibliographic record
Abstract
OBJECTIVE: Spontaneous spinal epidural hematoma (SSEH) manifests from blood accumulating in the epidural space, compressing the spinal cord and leading to acute neurological deficits. Standard therapy is decompressive laminectomy, although spontaneous recoveries have been reported. Sub-optimal therapeutic principles contribute to SSEH's 5.7% mortality-which patient will benefit from surgery remains unclear. This study aims to investigate parameters that affect SSEH's progression, outlining a best-practice therapeutic approach. MATERIALS AND METHODS: Literature review yielded 65 cases from 12 studies. Furthermore, 6 cases were presented from our institution. All data were analyzed under American Spinal Injury Association (ASIA) score guidelines. RESULTS: Fifty percent of SSEH patients do not fully recover. In all, 30% of patients who presented with an ASIA score of A did not improve with surgery, although every SSEH patient who presented at C or D improved. Spontaneous recovery is rare-only 23% of patients were treated conservatively. Seventy-three percent of those made a full recovery, as opposed to the 48% improvement in patients managed surgically. Thirty-three percent of patients managed conservatively had an initial score of A or B, all improving to a score of D or E without surgery. Regardless, conservative management tends toward low-risk presentations. Patients managed conservatively were three times as likely to have an initial score of D than their surgically managed counterparts. DISCUSSION: The degree of pre-operative neural deficit is a major prognostic factor. Conservative management has proven effective, although feasible only if spontaneous recovery is manifested. Decompressive laminectomy should continue to remain readily available, given the inverse correlation between operative interval and recovery.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".