The skin microbiome in psoriatic disease: A systematic review and critical appraisal
Bibliographic record
Abstract
Background Psoriasis affects 1–3% of the Canadian population. Psoriatic arthritis (PsA), the most common comorbidity of psoriasis, affects up to 30% of psoriasis patients. The skin microbiome is hypothesized to play a role in the pathogenesis of psoriatic disease (PsD-psoriasis and PsA). Objective To summarize the current state of literature on the skin microbiome in PsD. Methods A systematic review was performed using searches in Ovid, Medline, Embase, Medline Epub Ahead of Print and In-Process & Other Non-Indexed Citations, and Cochrane Central Register of Controlled Trials (CENTRAL). Search was limited to humans and English language, with no limits for date or publication type. Results Of 4,032 citations identified, 9 studies met inclusion criteria (7 on psoriasis only and 2 studies compared the microbiome characteristics between psoriasis and PsA). Compared to healthy controls, lesions demonstrated a decreased alpha diversity, higher relative abundances of Firmicutes, and lower relative abundances of Actinobacteria. Less conclusive were genus-level results, which nonetheless demonstrated trends towards increased Streptococcus , Staphylococcus , and Corynebacterium and decreased Propionibacterium in lesions vs. control. Limitations Study designs were heterogeneous, including sampling technique and exclusion criteria. Conclusions Phyla- and selected genus-level characteristic of the psoriatic microbiome are presented; further research is warranted.
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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 | 0.013 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".