The association of serum uric acid levels in psoriasis patients
Bibliographic record
Abstract
BACKGROUND: Current research has proved that psoriasis is associated with serum uric acid (SUAC) levels. Our purpose is to clarify SUAC levels and the incidence of hyperuricemia in psoriasis patients, and to compare SUCA levels in different groups' psoriasis patients. METHODS: We plan to search 7 electronic bibliographic databases (PubMed, Embase, Cochrane, and 4 Chinese databases) from inception to August 2019. Literatures selection and data collection will be performed independently by 2 authors. The Newcastle-Ottawa scale will be used to assess the methodologic quality and bias of included studies. Firstly, standard pairwise meta-analysis will be used to examine the considered data synthesis. Secondly, if the identified studies appear sufficiently similar within and across the different comparisons between different groups of psoriasis patients, we will estimate SUAC levels using network meta-analysis in different age and ethnicity psoriasis patients. Mean difference, risk ratio, and 95% confidence intervals will be used to assess the SUAC levels and the incidence of hyperuricemia in psoriasis patients. The software of Stata and WinBUGS will be used to calculations. RESULTS: The results will be published in a peer-reviewed journal. CONCLUSION: Our study will compare SUCA levels in different groups' psoriasis patients through network meta-analysis, and we believe our job is very meaningful. ETHICS AND DISSEMINATION: Our study is a secondary study of the existing literature. So, ethical and dissemination approval is not required.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".