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 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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.032 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".