Protocol for a preclinical systematic review and meta-analysis of pharmacological targeting of peroxisome proliferator-activated receptors in experimental renal injury
Bibliographic record
Abstract
INTRODUCTION: Impaired lipid metabolism in the renal tubule plays a prominent role in the progression of renal fibrosis following acute kidney injury (AKI) and in chronic kidney disease (CKD). Peroxisome proliferator-activated receptors (PPARs) are promising druggable targets to mitigate renal fibrosis by redirecting metabolism, including restoration of fatty acid oxidation (FAO) capacity. We aim to synthesise evidence from preclinical studies of pharmacological PPAR targeting in experimental renal injury, and inform the design of future studies evaluating PPAR-mediated restoration of FAO in AKI and CKD. METHODS AND ANALYSIS: Studies reporting on the impact of pharmacological PPAR modulation in animal models of renal injury will be collected from MEDLINE (Ovid), Embase and Web of Science databases. Predefined eligibility criteria will exclude studies testing medications which are not specific ligands of one or more PPARs and studies involving multimodal pharmacological treatment. The Systematic Review Centre for Laboratory Animal Experimentation risk of bias tool and Collaborative Approach to Meta-Analysis and Review of Animal Experimental Studies checklist will be used to assess quality of the included studies. Data extraction will be followed by a narrative synthesis of the data and meta-analysis where feasible. Analysis will be performed separately for AKI, CKD and renal transplant models. Subgroup analyses will be performed based on study design characteristics, PPAR isotype(s) targeted, and classes of PPAR-targeting medications used. Risk of publication bias will be assessed using funnel plotting, Egger's regression and trim-and-fill analysis. ETHICS AND DISSEMINATION: Ethical approval is not required. Findings will be published in a peer-reviewed journal and presented at scientific meetings. PROSPERO REGISTRATION NUMBER: CRD42021265550.
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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 | 0.077 | 0.124 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.019 | 0.021 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.121 | 0.014 |
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".