Biomarkers in rosacea: A systematic review
Bibliographic record
Abstract
Abstract Rosacea is a chronic and psychologically ladened disease affecting 1%–3% of people worldwide. The identification and validation of biomarkers in rosacea patients has the potential to improve disease progression, support diagnosis, provide objective measures for clinical trials and aid in management. The objective of this review is to systematically identify all rosacea biomarkers, categorize them by type and identify trends to improve disease expression. Eligibility criteria for this review (PROSPERO CRD42023397510) include randomized controlled trials, case–control studies, cohort studies and other observational studies. No restrictions were placed on patient demographics (age, sex, ethnicity) or language of publication until February 2023. Quality of studies was assessed using the National Institute of Health quality assessment tool. The literature search was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta‐analyses (PRISMA) guidelines. A total of 805 unique articles were screened based on the applied inclusion and exclusion criteria. After the articles were screened based on title/abstract and full‐text, a total of 38 studies were included, reporting on a total of 119 unique biomarkers. The results of this review and current rosacea pathogenic mechanisms provide the greatest support for the innate cathelicidin and inflammasome, T h 1 and T h 17 pathways. The most commonly reported biomarkers include IL‐1β, TNF‐α, IL‐37, IFN‐γ and MMP‐9. Biomarkers identified in this study support current theories of rosacea pathogenesis and provide direction for research to further our knowledge. However, more research is needed to identify biomarkers panels that can provide diagnostic utility. This may be difficult due to the heterogeneity of the disease and potential differences between rosacea subtypes.
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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 | 0.008 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.010 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".