Management and prevention of drug resistant infections in burn patients
Bibliographic record
Abstract
Introduction: Despite modern advances, the primary cause of death after burns remains infection and sepsis. A key factor in determining outcomes is colonization with multi-drug resistant (MDR) organisms. Infections secondary to MDR organisms are challenging due to lack of adequate antibiotic treatment, subsequently prolonging hospital stay and increasing risk of adverse outcomes.Areas covered: This review highlights the most frequent organisms colonizing burn wounds as well as the most common MDR bacterial infections. Additionally, we discuss different treatment modalities and MDR infection prevention strategies as their appropriate management would minimize morbidity and mortality in this population. We conducted a search for articles on PubMed, Web of Science, Embase, Cochrane, Scopus and UpToDate with applied search strategies including a combination of: “burns, ‘thermal injury,’ ‘infections,’ ‘sepsis,’ ‘drug resistance,’ and ‘antimicrobials.’Expert opinion: Management and prevention of MDR infections in burns is an ongoing challenge. We highlight the importance of preventative over therapeutic strategies, which are easy to implement and cost-effective. Additionally, targeted, limited use of antimicrobials can be beneficial in burn patients. A promising future area of investigation within this field is post-trauma microbiome profiling. Currently, the best treatment strategy for MDR in burn patients is prevention.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".