MicroRNA in Human Acute Kidney Injury: A Systematic Review Protocol
Bibliographic record
Abstract
BACKGROUND: Acute kidney injury (AKI) is a common complication of hospitalization with high morbidity and mortality for which no effective treatments exist and for which current diagnostic tools have limitations for earlier identification. MicroRNAs (miRNAs) are small non-coding RNAs that have been implicated in the pathogenesis of AKI, and some miRNAs have shown promise as therapeutic tools in animal models of AKI. However, less is known about the role of miRNAs in human AKI. OBJECTIVE: To evaluate the role of miRNAs in human subjects with AKI. DESIGN: Systematic review and meta-analysis. MEASUREMENTS: Quantification of miRNA levels from human blood, urine, or kidney biopsy samples, and measures of renal function as defined in the study protocol. METHODS: A comprehensive search strategy for Ovid MEDLINE All, Embase, Web of Science, and CENTRAL will be developed to identify investigational studies that evaluated the relationship between miRNA levels and human AKI. Primary outcomes will include measurements of kidney function and miRNA levels. Study screening, review and data extraction will be performed independently by 2 reviewers. Study quality and certainty of evidence will be assessed with validated tools. A narrative synthesis will be included and the possibility for meta-analysis will be assessed according to characteristics of clinical and statistical heterogeneity between studies. LIMITATIONS: These include (1) lack of randomized trials of miRNAs for the prevention or treatment of human AKI, (2) quality of included studies, and (3) sources of clinical and statistical heterogeneity that may affect strength and reproducibility of results. CONCLUSION: Previous studies of miRNAs in different animal models of AKI have generated strong interest on their use for the prevention and treatment of human AKI. This systematic review will characterize the most promising miRNAs for human research and will identify methodological constraints from miRNA research in human AKI to help inform the design of future studies. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42020201253.
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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 | 0.055 | 0.069 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.018 | 0.015 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.061 | 0.007 |
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".