Diagnostic Value of PICP and PIIINP in Myocardial Fibrosis: A Systematic Review and Meta‐analysis
Bibliographic record
Abstract
Myocardial fibrosis is the excessive accumulation of extracellular matrix (ECM) components such as collagen and fibronectin, and its clinical diagnosis is always with limitations. Recently, PICP and PIIINP have been reported by several studies as potential biomarkers for the diagnosis of myocardial fibrosis, however, no meta-analyses focusing on the diagnostic values of these biomarkers have been conducted. So, the present study aimed to investigate the clinical diagnostic value of PICP and PIIINP in myocardial fibrosis patients. Based on the inclusion criteria, 1130 records were identified from four databases, and 12 studies were included eventually after independent screening. All 12 studies were high quality with the Newcastle-Ottawa Quality Assessment Scale (NOS) values ≥7. The results of the present meta-analysis indicated that patients with myocardial fibrosis revealed significantly elevated serum PICP (standard mean difference [SMD] = 0.90, 95% confidence interval [95% CI] = 0.40 to 1.40) and PIIINP (SMD = 0.83, 95% CI = 0.04 to 1.23). Therefore, we believe that PICP and PIIINP could be used as potential auxiliary biomarkers in the clinical diagnosis of myocardial fibrosis. This article is protected by copyright. All rights reserved.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.019 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".