The Impact of Acne Treatment on Skin Bacterial Microbiota: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: have been examined as contributors to the pathogenesis of acne. Given the prevalence of the disease among adolescents and adults, the overutilization of antimicrobial agents may breed resistance and alter commensal microflora. OBJECTIVES: To characterize the impact of acne treatment on the diversity and relative abundance of the cutaneous microbial community, particularly of the bacterial flora. METHODS: An electronic search was conducted of Embase, MEDLINE, and the Cochrane Central Register of Controlled Trials (CENTRAL) on June 5, 2020. Interventional and observational studies examining patients receiving acne treatment with culture-independent, community-level analysis of the cutaneous microbiome were included. RESULTS: Nine studies with 170 treated acne patients were included. Five studies reported a significant change in alpha diversity following treatment, 3 of which examining systemic antibiotics reported significant increases in diversity. Two of 3 studies examining effects of benzoyl peroxide reported a decrease in diversity. However, trends in diversity were heterogeneous among studies. CONCLUSIONS: While individual variability in microbiome composition, and study-level heterogeneity in study sampling techniques may limit quantitative synthesis, our results support findings that acne treatment, including those not considered to have antimicrobial properties, alters the composition of the cutaneous microbiome.PROSPERO registration: CRD42020190629.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.007 | 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) | 0.005 | 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".