P.135 Epidemiology of spine infection in patients with history of IV drug use and HIV infection. Possibility of the secondary prevention
Bibliographic record
Abstract
Background: One of the major risk factors for spine infection is IV drug use and HIV infection. An increase in these risk factors has coincided with increased rates of spinal infection in Saskatchewan. However, the exact incidence and the clinical significance of spine infection associated with high-risk behavior is poorly understood. Methods: A retrospective review was completed for adult patients with discitis, osteomyelitis, or epidural abscess admitted to the Royal University Hospital, University of Saskatchewan over the last eight years. Results: This study included 176 patients consisting of 41% with discitis, 69% with osteomyelitis and 45% with epidural abscess. Overall mortality was 3% and 16% of patients developed severe disability.40% of patients were intravenous drug users, 45% were hepatitis C positive and 12% were HIV positive. For the initial four years of our study we experienced 72 patients. We experienced 91 cases over the past four years. Geographical analysis showed high incidence areas within the city of Saskatoon. Conclusions: High rates of IV drug use, Hepatitis C, and HIV have important implications in terms of what measures would assist in prevention of this condition. Secondary prevention or early identification of patients may reduce the number of patients who require lengthy admission, surgery and long term care for disablity.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".