Antibiotic resistance: shifting the paradigm in topical acne treatment.
Bibliographic record
Abstract
INTRODUCTION: Multiple topical therapies are available for mild to moderate acne vulgaris. The role of antibiotics and their resistance in the treatment of acne was reviewed by an expert panel of dermatologists who practice in Canada. METHODS: Prior to the consensus meeting, the panel members filled out a survey on their current practice using topical treatment for acne. A literature review was carried out using information obtained from PubMed, Cochrane Library, Medline, and EMBASE. During a consensus meeting organized at the Spring Dermatology Update on April 27, 2014 in Toronto, ON, the panel had a blind vote on the issues at hand. RESULTS: The panel reached consensus on: 1) Antibiotics are an integral part of acne treatment not only due to their antibiotic effect but also by their anti-inflammatory action. 2) Oral antibiotics should be used for a short period of time if possible. 3) Topical antibiotics should not be used in monotherapy. 4) Retinoids are effective in reducing antibiotic resistance. 5) A benzoyl peroxide wash is as effective as topical benzoyl peroxide in reducing antibiotic resistance. 6) Therapy needs to be re-evaluated in 6-8 weeks versus 12 weeks. The recommendations given by the panel are to be disseminated to both general practitioners and dermatologists. CONCLUSION: For mild to moderate acne treatment, topical antibiotics in monotherapy are not to be used but may be combined with a retinoid or BPO to safely achieve more successful outcomes.
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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 | 0.014 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".