Abnormal Serum Copper and Zinc Levels in Patients with Psoriasis: A Meta-Analysis
Bibliographic record
Abstract
Background: Copper and zinc are important trace elements involved in the development of psoriasis. However, reports regarding changes in serum copper and zinc levels in patients with psoriasis have been inconsistent. Aims: This meta-analysis was designed to analyze changes in serum copper and zinc levels between patients with psoriasis and a healthy population. Materials and Methods: English and Chinese literature from international and national electronic databases from 1988 to May 2016 was analyzed. Studies that performed a comparative analysis of serum copper and zinc levels between patients with psoriasis and healthy controls were included in the meta-analysis. The random-effects model was used to calculate the overall combined estimates of serum copper and zinc levels between patients with psoriasis and healthy individuals. Results: Fifteen references were included in this study, including 1324 patients with psoriasis and 1324 healthy controls. Compared with healthy controls, serum copper levels were significantly increased (Z = 4.02, P < 0.0001; standardized mean difference [SMD], 1.23; 95% confidence interval [CI], 0.63 to 1.82), and serum zinc levels were significantly decreased (Z = 2.95, P < 0.0001; SMD, −1.35; 95% CI, −2.25 to − 0.45) in patients with psoriasis. Conclusions: In conclusion, increased serum copper and decreased serum zinc levels were generally observed in patients with psoriasis. Treatments to normalize the serum copper and zinc levels may improve the outcome of psoriasis patients.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.031 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".