Reducing Biofilm Infections in Burn Patients’ Wounds and Biofilms on Surfaces in Hospitals, Medical Facilities and Medical Equipment to Improve Burn Care: A Systematic Review
Bibliographic record
Abstract
Biofilms in burns are major problems: bacterial communities rapidly develop antibiotic resistance, and 60% of burn mortality is attributed to biofilms. Key pathogens are Pseudomonas aeruginosa, methicillin-resistant Staphylococcus aureus, and multidrug-resistant Acinetobacter baumanii. Purpose: identify current and novel interventions to reduce biofilms on patients’ burns and hospital surfaces and equipment. Medline and Embase were searched without date or language limits, and 31 possible interventions were prioritised: phages, nano-silver, AgSD-NLs@Cur, Acticoat and Mepilex silver, acetic acid, graphene-metal combinations, CuCo2SO4 nanoparticles, Chlorhexidene acetate nanoemulsion, a hydrogel with moxifloxacin, carbomer, Chitosan and Boswellia, LED light therapy with nano-emodin or antimicrobial blue light + Carvacrol to release reactive oxygen species, mannosidase + trypsin, NCK-10 (a napthalene compound with a decyl chain), antimicrobial peptide PV3 (includes two snake venoms), and polypeptides P03 and PL2. Most interventions aimed to penetrate cell membranes and reported significant reductions in biofilms in cfu/mL or biofilm mass or antibiotic minimal inhibitory concentrations or bacterial expression of virulence or quorum sensing genes. Scanning electron microscopy identified important changes in bacterial surfaces. Patients with biofilms need isolating and treating before full admission to hospital. Cleaning and disinfecting needs to include identifying biofilms on keyboards, tablets, cell phones, medical equipment (especially endoscopes), sinks, drains, and kitchens.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".