Epidural Hematoma Following Cervical Spine Surgery
Bibliographic record
Abstract
Study Design: A multicentered retrospective case series. Objective: To determine the incidence and circumstances surrounding the development of a symptomatic postoperative epidural hematoma in the cervical spine. Methods: Patients who underwent cervical spine surgery between January 1, 2005, and December 31, 2011, at 23 institutions were reviewed, and all patients who developed an epidural hematoma were identified. Results: A total of 16 582 cervical spine surgeries were identified, and 15 patients developed a postoperative epidural hematoma, for a total incidence of 0.090%. Substantial variation between institutions was noted, with 11 sites reporting no epidural hematomas, and 1 site reporting an incidence of 0.76%. All patients initially presented with a neurologic deficit. Nine patients had complete resolution of the neurologic deficit after hematoma evacuation; however 2 of the 3 patients (66%) who had a delay in the diagnosis of the epidural hematoma had residual neurologic deficits compared to only 4 of the 12 patients (33%) who had no delay in the diagnosis or treatment ( P = .53). Additionally, the patients who experienced a postoperative epidural hematoma did not experience any significant improvement in health-related quality-of-life metrics as a result of the index procedure at final follow-up evaluation. Conclusion: This is the largest series to date to analyze the incidence of an epidural hematoma following cervical spine surgery, and this study suggest that an epidural hematoma occurs in approximately 1 out of 1000 cervical spine surgeries. Prompt diagnosis and treatment may improve the chance of making a complete neurologic recovery, but patients who develop this complication do not show improvements in the health-related quality-of-life measurements.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".