Do Antimicrobial Resistance Patterns Matter? An Algorithm for the Treatment of Patients With Impetigo
Bibliographic record
Abstract
Background: Impetigo, a highly contagious bacterial skin infection commonly occurring in young children, but adults may also be affected. The superficial skin infection is mainly caused by Staphylococcus aureus (S. aureus) and less frequently by Streptococcus pyogenes (S. pyogenes). Antimicrobial resistance has become a worldwide concern and needs to be addressed when selecting treatment for impetigo patients. An evidence-based impetigo treatment algorithm was developed to address the treatment of impetigo for pediatric and adult populations. Methods: An international panel of pediatric dermatologists, dermatologists, pediatricians, and pediatric infectious disease specialists employed a modified Delphi technique to develop the impetigo treatment algorithm. Treatment recommendations were evidence-based, taking into account antimicrobial stewardship and the increasing resistance to oral and topical antibiotics. Results: The algorithm includes education and prevention of impetigo, diagnosis and classification, treatment measures, and follow-up and distinguishes between localized and widespread or epidemic outbreaks of impetigo. The panel adopted the definition of localized impetigo of fewer than ten lesions and smaller than 36 cm2 area affected in patients of two months and up with no compromised immune status. Resistance to oral and topical antibiotics prescribed for the treatment of impetigo such as mupirocin, retapamulin, fusidic acid, have been widely reported. Conclusions: When prescribing antibiotics, it is essential to know the local trends in antibiotic resistance. Ozenoxacin cream 1% is highly effective against S. pyogenes and S. aureus, including methycyllin-susceptible and resistant strains (MRSA), and may be a suitable option for localized impetigo.J Drugs Dermatol. 20(2):134-142. doi:10.36849/JDD.2021.5475 THIS ARTICLE HAD BEEN MADE AVAILABLE FREE OF CHARGE. PLEASE SCROLL DOWN TO ACCESS THE FULL TEXT OF THIS ARTICLE WITHOUT LOGGING IN. NO PURCHASE NECESSARY. PLEASE CONTACT THE PUBLISHER WITH ANY QUESTIONS.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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".