Management of cervical spine epidural abscess: a systematic review
Bibliographic record
Abstract
Background: Cervical spinal epidural abscess (CSEA) is a localized infection between the thecal sac and cervical spinal column which may result in neurological deficit and death if inadequately treated. Two treatment options exist: medical management and surgical intervention. Our objective was to analyze CSEA patient outcomes in order to determine the optimal method of treatment. Methods: An electronic literature search for relevant case series and retrospective reviews was conducted through June 2016. Data abstraction and study quality assessment were performed by two independent reviewers. A lack of available data led to a post hoc decision not to perform meta-analysis of the results; study findings were synthesized qualitatively. Results: 927 studies were identified, of which 11 were included. Four studies were ranked as good quality, and seven ranked as fair quality. In total, data from 173 patients were included. Mean age was 55 years; 61.3% were male. Intravenous drug use was the most common risk factor for CSEA development. Staphylococcus aureus was the most commonly cultured pathogen. 140 patients underwent initial surgery, an additional 18 patients were surgically treated upon failure of medical management, and 15 patients were treated with antibiotics alone. Conclusion: The rates of medical management failure described in our review were much higher than those reported in the literature for thoracolumbar spinal epidural abscess patients, suggesting that CSEA patients may be at a greater risk for poor outcomes following nonoperative treatment. Thus, early surgery appears most viable for optimizing CSEA patient outcomes. Further research is needed in order to corroborate these recommendations.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".