Comorbid diagnosis of systemic lupus erythematosus in vitiligo: a systematic review and meta-analysis
Bibliographic record
Abstract
https://doi.org/10.1093/bjd/ljac038 Dear Editor, Vitiligo is an autoimmune pigmentary disorder of the skin with varying prevalence across ethnic groups.1 Studies have suggested an association between systemic lupus erythematosus (SLE) and vitiligo; however, a systematic evaluation of this association and its prevalence across populations are lacking. Herein, we aim to address this knowledge gap through a systematic review and meta-analysis. MEDLINE and EMBASE were searched from inception to 12 January 2022, using the keywords ‘vitiligo’ and ‘lupus’ or ‘comorbidity’ according to PRISMA guidelines (PROSPERO CRD42021240524). Of 880 individual studies, 21 were included (see Supporting Information, Figure S1), comprising 119 155 patients with vitiligo from various populations (see Supporting Information, Tables S1 and S2). Study quality was rated on the Newcastle–Ottawa scale. Studies were pooled via an inverse-variance weighted random-effects meta-analysis using the DerSimonian and Laird method, modelled after published literature.2 (The data that support the findings of this study are available from the corresponding author upon reasonable request.)
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.017 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".