The therapeutic potential of C-peptide in kidney disease: a protocol for a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Kidney disease remains a major cause of morbidity and mortality in Canada and worldwide. New medical treatments are needed to reduce the progression of kidney disease to improve patient outcomes. C-peptide is normally released by pancreatic beta-cells along with insulin in healthy individuals, and has been shown to have intrinsic biological activity and to potentially be renoprotective. The effect of exogenous C-peptide on kidney structure and function, and the role of C-peptide in the treatment of kidney disease have not yet been fully elucidated. METHODS/DESIGN: We will conduct a systematic review of the literature in human clinical trials and mammalian experimental models to ascertain the current evidence for the role of C-peptide as a potential therapeutic agent for the treatment of kidney disease. We aim to identify whether exogenously delivered C-peptide has an effect on clinically relevant outcomes such as glomerular filtration rate, proteinuria, kidney histology, requirement of renal replacement therapy, and mortality. We will search MEDLINE, EMBASE, and the Cochrane Central Databases for human or animal studies in which C-peptide was administered and renal endpoints were subsequently measured. Study quality will be assessed using the Cochrane Collaboration's tool for assessing risk of bias. If appropriate, a meta-analysis will be performed as per standard techniques. DISCUSSION: The results of this study will determine the potential role of C-peptide as a therapeutic intervention for patients with kidney disease and will help guide subsequent clinical trials. The study may also provide insight into which patients or disease states are likely to benefit the most from C-peptide. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42014007472.
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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 | 0.074 | 0.116 |
| Meta-epidemiology (narrow) | 0.006 | 0.004 |
| Meta-epidemiology (broad) | 0.027 | 0.036 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.036 | 0.003 |
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".