Incidence of melanoma and non-melanoma skin cancer in patients with celiac disease: a systematic review and meta-analysis
Bibliographic record
Abstract
There is conflicting observational evidence regarding the association between skin cancer and celiac disease (CD). The purpose of this review was to investigate the incidence rate ratio (IRR) of skin cancer incidence between patients with and without CD. MEDLINE and EMBASE databases were searched on October 27th, 2021 and eight articles were identified for review. Quality assessment was conducted using the Newcastle Ottawa Scale. Seven articles were included in meta-analysis for a pooled estimate of IRR across all skin cancers, malignant melanoma (MM), and non-melanoma skin cancers (NMSC). In total, 74,860 CD patients were followed for 710,214 person-years in the meta-analysis. Overall combined incidence was 99.8 cases per 100,000 person-years. Meta-analysis of all skin cancer incidence showed no significant difference in CD patients compared to controls (IRR: 1.06; 95% CI: 0.95, 1.17; p=0.29; I2: 0%). Five studies reported on MM incidence; there was no significant difference in incidence compared to controls (IRR: 0.87, 95% CI: 0.72, 1.06; p=0.22; I2: 9%). Five studies reported on NMSC incidence, revealing a significantly increased risk of NMSC in CD patients (IRR: 1.14; 95% CI: 1.01, 1.28; p=0.04; I2: 0%). Our findings suggest a significantly increased incidence of NMSC in CD patients compared to control data and no significant association between CD and MM incidence. The findings are limited by the quantity and quality of the evidence. Nonetheless, clinicians should emphasize the importance of sun protection, such as sunscreen usage and self-examination for patients with CD.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.035 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".