Prevalence of autoimmune thyroid disease in patients with psoriasis: a meta-analysis
Bibliographic record
Abstract
OBJECTIVE: Psoriasis is a chronic inflammatory disease with autoimmune aetiology. A possible link between psoriasis and autoimmune thyroid disease (AITD) has been suggested in some studies with inconsistent findings. This meta-analysis aims to determine the association between psoriasis and AITD. DESIGN: A meta-analysis of observational studies. DATA SOURCES: PubMed, EMBASE, Scopus and the Cochrane Library were searched up to 1 November 2021. ELIGIBILITY CRITERIA FOR SELECTING STUDIES: We included non-randomised studies, each with over 50 cases in every group, focusing on the rate of comorbidity between psoriasis and AITD. DATA EXTRACTION AND SYNTHESIS: statistic. The Newcastle-Ottawa Scale and Agency for Healthcare Research and Quality were applied for quality assessment. The risk of bias was assessed with Risk Of Bias In Non-randomised Studies-of Interventions (ROBINS-I). RESULTS: Eleven available studies with data on 253 313 patients with psoriasis and 1 376 533 controls were included. Meta-analysis showed that patients with psoriasis had a higher prevalence of AITD (OR 1.76, 95% CI 1.35 to 2.28, Z=4.25, p<0.01), especially loss-of-function disorder of the thyroid gland. Both thyroglobulin antibodies positive rate (OR 1.98, 95% CI 1.27 to 3.10, Z=3.00, p<0.01) and thyroid peroxidase antibodies positive rate (OR 2.15, 95% CI 1.31 to 3.52, Z=3.05, p<0.01) were also increased in the psoriasis group compared with the control group. CONCLUSIONS: Our study indicates that the rate of co-occurring AITD was significantly increased in patients with psoriasis. It suggests that the increased risk of AITD should be concerned in patients with psoriasis. PROSPERO REGISTRATION NUMBER: CRD42020206005.
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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 | 0.016 | 0.029 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.085 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 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".