Epidemiology of spinal infections: retrospective review of the patients with osteomyelitis, discitis, and epidural abscesses
Bibliographic record
Abstract
Background: Spinal infections are one of the most difficult, complex, and multi-disciplinary health conditions. The purpose of this paper was to gather demographic information of the patients with spinal infections and to identify factors that would influence their management. Methods: Retrospective chart review of 146 adult patients with osteomyelitis, discitis or epidural abscesses admitted to the Royal University Hospital, Saskatchewan, from 2007-2014. Results: Patient demographics included 59% male, 41% female, mean age 53 years. 36% of patients required surgery, 44% were IV drug users, and 71% were managed by surgeons. Presence of a neurological deficit, higher white blood cell count, and longer hospital admission, in relation to poor outcomes, were statistically significant. Higher age and shorter duration between onset of symptoms and admission showed a trend toward a poorer outcome. Epidural abscess and presence of a neurological deficit are variables isolated as being statistically significant in relation to need for surgery. 57.1% of patients with epidural abscess and 51.7% with neurological deficit required surgery. Conclusions: We were able to identify high-risk patients as to the need for surgery and poor outcome. Based on this information, we can better tailor our management strategy of this difficult condition.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".