Wounds, Antimicrobial Resistance and Challenges of Implementing a Surveillance System in Myanmar: A Mixed-Methods Study
Bibliographic record
Abstract
Wound infections with drug-resistant bacteria lead to higher mortality and morbidity and increased healthcare costs. We aimed to describe the spectrum of bacterial pathogens, isolated from wound cultures in Yangon General Hospital in 2018, and their antimicrobial resistance (AMR) patterns and to understand the challenges in implementing an AMR surveillance system in Myanmar. We conducted a concurrent mixed-methods study involving analysis of surveillance data and in-depth interviews with nine key personnel involved in AMR surveillance. Of 1418 wound specimens processed, 822 (58%) were culture-positive. The most common Gram-positive bacteria were coagulase-negative staphylococci (23.3%) and Staphylococcus aureus (15.1%). Among Gram-negative bacteria, Escherichia coli (12.5%) and Pseudomonas aeruginosa (10.1%) were common. Staphylococcus aureus isolates were resistant to penicillin (98%), oxacillin (70%) and tetracycline (66%). Escherichia coli showed resistance to ampicillin (98%). Lack of dedicated and trained staff (microbiologist, technician, data entry operator), lack of computers at sentinel sites and non-uniform and non-standardized data capture formats were the major challenges in implementing AMR surveillance. These challenges need to be addressed urgently. We also recommend periodic analysis and sharing of antibiograms at every hospital to inform the treatment regimens used in wound management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".