Incidence and Risk Factors Associated with Microbial Colonization of Burn Wounds: An Observational Study
Bibliographic record
Abstract
Background: Microbial colonization of burn wounds leads to infection, which is a major cause of morbidity/mortality, prolonged admission, and cost. This study aims to investigate the incidence of positive burn wound colonization and its associated risk factors in a provincial referral center within a single-payer system. Methods: We performed a retrospective review of all adult (≥18 years) patients admitted to a single, tertiary burn center, with a primary burn diagnosis between January 2011 and 2021. Microbiology records were screened to identify patients with culture-positive burn wounds. Univariable and multivariable logistic regression analyses were used to evaluate risk factors associated with burn site colonization. Results: The sample included N = 634 participants. Most were male (72.1%), with a flame injury (62%), and had a mean age of 47.6 (±18.0) years and a TBSA of 13.5% (±14.8). The incidence of positive burn wound colonization was 27.3%. Increasing participant age, diabetic status, larger burn TBSA, presence of full-thickness burns, inhalation injury, and lower limb and trunk involvement were associated with statistically significant ( P ≤ .05) increased odds of a positive burn wound culture. Conclusion: This study provides an estimate of the incidence of primary burn wound colonization at a single, tertiary care, burn center as well as identifies potential risk factors associated with this outcome. Clinicians should consider closely monitoring patients with these risk factors for possible progression to clinical burn site infection. Future research should address strategies to mitigate colonization in patients with identified risk factors.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".