Incidence and risk factors for failed medical management of spinal epidural abscess: a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE Spinal epidural abscess (SEA) is a life-threatening infection. It is uncertain whether medical versus surgical treatment is the ideal initial approach for neurologically intact patients with SEA. Recent evidence demonstrates that initial medical management is increasingly common; however, patients who ultimately require surgery after failed medical management may have a worse prognosis than those whose treatment was initially surgical. The primary objective of this study was to establish the current incidence of failed medical management for SEA. The secondary aim was to identify risk factors associated with the failure of medical management. METHODS The authors conducted a systematic review and meta-analysis by searching electronic databases (MEDLINE, Embase, CINAHL, and PubMed), recent conference proceedings, and reference lists of relevant articles. Studies that reported original data on consecutive adult patients with SEA treated medically were eligible for inclusion. RESULTS Twelve studies met the inclusion criteria, which included a total of 489 medically treated patients with SEA. Agreement on articles for study inclusion was very high between the reviewers (kappa 0.86). In a meta-analysis, the overall pooled risk of failed medical management was 29.3% (95% CI 21.4%-37.2%) and when medical to surgical crossover was used to define failure the rate was 26.3% (95% CI 13.0%-39.7%). Only 6 studies provided data for analysis by intended treatment, with a pooled estimate of 35.1% (95% CI 15.7%-54.4%) of failed medical management. Two studies reported predictors of the failure of medical management. CONCLUSIONS Although the incidence of failed medical management of SEA was relatively common in published reports, estimates were highly heterogeneous between studies, thus introducing uncertainty about the frequency of this risk. A consensus definition of failure is required to facilitate comparison of failure rates across studies.
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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 | 0.013 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.036 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".