Comorbidities Associated With Lichen Planopilaris: A Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
Lichen planopilaris (LPP) is a primary cicatricial alopecia suggested to be associated with various comorbid medical conditions, including autoimmune, endocrine, and metabolic disorders. However, there is conflicting data regarding these associations. This study quantitatively evaluated the prevalence and odds of comorbidities among patients with LPP. PubMed/MEDLINE, EMBASE, Cochrane Library, and Web of Science were searched from inception to December 25, 2023, to include observational studies reporting prevalent/incident cases of at least one comorbidity in LPP patients. The study adhered to the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA). Risk-of-bias assessment was performed using the Newcastle-Ottawa scale. Random-effect models were utilized to analyze available data. Of the 2535 studies screened, 59 studies were included for meta-analysis, comprising 15,178 LPP patients and 64,735,181 non-LPP controls. Among LPP patients, comorbidities with the highest risk (odds ratio, OR [95% confidence interval, Cl]; prevalence) compared with non-LPP controls and data available from more than one study included lichen planus (15.78 [1.79-139.30]; 6.5%), psoriasis (3.55 [1.10-11.51]; 4.3%), rosacea (3.15 [2.25-4.40]; 10.4%), melanoma (3.11 [1.41-6.87]; 2.3%), systemic lupus erythematosus (3.06 [2.29-4.09]; 1.8%), cutaneous squamous cell carcinoma (3.04 [1.64-5.66]; 4.8%), vitiligo (2.89 [1.60-5.24]; 2.0%), coronary artery disease (1.66 [1.46-1.88]; 7.6%), rheumatoid arthritis (1.50 [1.22-1.84]; 3.5%), hypothyroidism (1.48 [1.12-1.96]; 17.8%), thyroiditis (1.26 [1.08-1.48]; 3.9%), and iron deficiency anemia (1.18 [1.05-1.33]; 9.1%). LPP is significantly associated with autoimmune disorders, thyroid diseases, and skin cancer. Further investigation is required to elucidate the underlying pathogenesis and establish appropriate screening criteria.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.037 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".