Epidemiology of upper limb skin and soft tissue infections requiring surgical intervention in Saskatoon, Canada: A retrospective chart review
Bibliographic record
Abstract
Abstract Background: Skin and soft tissue infections (SSTIs) are a leading cause of hospital admission and engagement with the health care system amongst people who inject drugs (PWID). The current study aims to describe the epidemiology of SSTIs requiring surgical intervention in Saskatoon, Canada. Methods: This retrospective chart review assessed patients with a primary diagnosis of upper limb SSTIs requiring surgical intervention at St. Paul’s Hospital and Royal University Hospital (Saskatoon, Canada) between January 1 and December 31, 2020. Results: 38 eligible patients with a median age of 34 years and M:F of 21:17 were identified. 31 (81.6%) smoked cigarettes and 19 (50.0%) used intravenous drugs. A majority of SSTIs were unilateral infections involving the hand 22 (57.9%) or upper arm 11 (28.9%). Ten (26.3%) patients had a prior SSTI requiring surgical management. Necrotizing fasciitis was diagnosed in 7 (18.4%) patients, two of which, required amputation of the affected hand or arm. The median length of hospital stay was 6 days (IQR: 4 – 14.5). Ten patients left the hospital against medical advice, before completion of treatment; of these patients, 8 (80.0%) were PWID. Conclusion: Harm reduction strategies may help address the rising incidence and recurrence of SSTIs in the injection drug use population. Involvement of addiction services and social work during hospital admission may reduce the rate of patient-directed discharge, facilitating the completion of treatment. Furthermore, increased access to needle exchange programs in the community may reduce the number of SSTIs caused by contaminated injection equipment in the PWID population.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".