311 Multiresistant Bacteria in Burns: The Relation Between Screening and Infection
Bibliographic record
Abstract
Methicillin-resistant Staphylococcus aureus (MRSA) is a key pathogen in burns patients. Several factors put them at increased risk of MRSA infection: partial loss of the skin barrier, the immune-compromising effects of burns, prolonged hospital stays, and invasive procedures. This study aims to find the relation between MRSA swab cultures (nasal, perianal, and wound) taken within 48h of admission plus weekly surveillance swab cultures and MRSA infection secondary to colonization. The data of all burns patients admitted to the provincial referral centre for burns in British Columbia from 2012 to 2016 were reviewed. MRSA cultures taken at admission and on weekly surveillance screening, including nasal, perianal, and wound-site swabs, were thoroughly reviewed. To determine associations between MRSA colonization and infection rates, both MRSA-positive and MRSA-negative swab cultures were included in the analysis. Several risk factors were considered: age, gender, ethnicity, %TBSA, BAUX index, inhalational injury, ICU admission and days, need for ventilator support and days, LOS in hospital, and complications. Univariate and multiple logistic regression analyses were used to predict correlations between positive swab cultures and risk factors. Data from 396 patients were analyzed. The median age at admission for the burns patients was 46.3 years. On admission, 2.53% of patients were MRSA positive, whereas 17.93% were found to be MRSA positive on weekly surveillance screening. Amongst this population, 60.56% developed MRSA infection secondary to MRSA colonization. Any positive swab culture and clinical risk factors are statistically associated (p < 0.001) with MRSA infection. At admission screening compliance for nasal and perianal swabs was higher than at surveillance but wound swab compliance at admission (20.2%) was lower than at surveillance (39.65%), although statistically associated with MRSA infection (p < 0.001). Nosocomial MRSA colonization rates are high, and patients incurring infections experience a greater than average number of procedures, wound-healing problems due to graft failure and wound complications, other integumentary complications, as well as respiratory, renal, and cardiovascular issues. Increased MRSA screening compliance and detection may prevent or reduce MRSA infection secondary to colonization.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".