Increased Serum Soluble Urokinase-Type Plasminogen Activator Receptor (suPAR) Levels in FSGS: A Meta-Analysis
Bibliographic record
Abstract
INTRODUCTION: The soluble urokinase-type plasminogen activator receptor (suPAR) has been found to be elevated in primary focal segmental glomerulosclerosis (pFSGS). However, its usefulness as a biomarker for FSGS remains controversial. We conducted a meta-analysis aiming at investigating the significance of suPAR in diagnosing pFSGS. METHODS: Electronic databases (PubMed and EMBASE) were searched to identify studies comparing suPAR levels in FSGS patients and controls, from the earliest available date to May 1, 2018. A random-effects model with standardized mean difference (SMD) was used for meta-analyses. Risk of bias was assessed using the Newcastle-Ottawa quality assessment scale. RESULTS: = 85%). Higher suPAR levels were also found in patients with pFSGS compared to those with minimal change disease (SMD 0.53, 95% CI 0.22 to 0.84). Of note, such a difference was not found in pediatric groups (SMD 0.42, 95% CI -0.13 to 0.96) while it was more evidently noted in adult patients (SMD 1.32, 95% CI 0.90 to 1.74). Serum suPAR levels did not differ between pFSGS patients in remission compared to those in active proteinuric state (SMD 0.29, 95% CI -0.30 to 0.88). Comparison with membranous nephropathy and IgA nephropathy showed no significant difference. CONCLUSIONS: Our meta-analysis demonstrated that, in comparison to both healthy controls and controls with minimal change disease, suPAR levels were significantly higher in adult patients with pFSGS. suPAR levels did not differ between pFSGS patients during the initial period of diagnosis and those in remission.
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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 | 0.013 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.050 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".