Serum Galectin-3 Level in Patients with Rheumatoid Arthritis: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Rheumatoid arthritis (RA) is a chronic inflammatory disease characterized by synovial tissue transformation and fibroblast-like synoviocyte (FLS) proliferation. Galectin-3 is gaining attention as a diagnostic and prognostic biomarker for RA diagnosis. Elevated levels of Galectin-3 cause RA-FLSs to stimulate and generate proinflammatory agents, contributing to cartilage degradation and osteoclast formation. This systematic review and meta-analysis aimed to evaluate published evidence and support future investigation of Galectin-3 as an early biomarker for RA. A systematic search was performed through four databases, including PubMed, the Web of Science, Scopus, and Embase, to find the studies examining Galectin-3 in individuals with RA compared to healthy controls. The risk of bias was evaluated using the Newcastle-Ottawa Quality Assessment Scale. Random-effects meta-analysis comparing serum/plasma Galectin-3 levels between individuals with RA and healthy control groups was performed to determine the standardized mean differences (SMD) along with 95% confidence intervals. Following the initial search, studies went through screening. 12 studies, involving 773 patients with RA and 411 healthy controls, were included. Meta-analysis of the included studies revealed that individuals with RA had significantly higher levels of circulatory Galectin-3 compared to healthy control groups (SMD 0.957, 95% CI 0.393 to 1.520). Univariable meta-regression showed no significant association between age, publication year, sample size, or the male percentage with effect size. According to the results, Galectin-3 might be useful as a biomarker for RA. To support these findings, further investigations of Galectin-3 as a possible early biomarker of RA is necessary.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.010 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".