Pancreatic stone protein as a novel biomarker of microvascular complications in type II diabetes mellitus: A systematic review and meta-analysis
Bibliographic record
Abstract
A BSTRACT Objectives: Pancreatic stone protein (PSP) has been identified as an indicator of systemic stress and is elevated in individuals diagnosed with type 2 diabetes mellitus (T2DM), potentially serving as a prognostic marker for both the onset and progression of the disease. Materials and Methods: This study was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis 2020 guidelines. Articles were sourced from MEDLINE, ProQuest, Science Direct, Google Scholar, and Cochrane Library electronic databases. Studies included are all observational studies examining PSP/Reg1α serum levels in patients with T2DM. The quality of the study was evaluated using the Newcastle–Ottawa Scale, as well as Review Manager 5.4 to perform the meta-analysis. Results: Seven studies met the criteria for inclusion. Pooled analysis revealed significant differences in PSP values between T2DM individuals and healthy controls (standardized mean difference [SMD] = 2.14, 95% confidence interval CI: 1.05–1.92, P < 0.00001). Further subgroup analysis showed PSP was substantially higher in T2DM with complications (SMD = −1.57, 95% CI: −2.12 to −1.02, P < 0.00001) compared to T2DM without complications (SMD = −1.39, 95% CI: −2.17 to − 0.61) and newly diagnosed T2DM (SMD = −1.85, 95% CI: −2.96 to −0.74). Grading of Recommendations, Assessment, Development, and Evaluations demonstrated moderate quality of evidence. Conclusion: Our analysis revealed a progressive elevation in PSP values concomitant with the worsening T2DM disease state across the entire spectrum. PSP exhibits promising potential as a biomarker for predicting both disease initiation and subsequent clinical course.
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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 | 0.012 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.041 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".