Bioactive scaffolds in stem-cell-based therapies for cardiac repair: protocol for a meta-analysis of randomized controlled preclinical trials in animal myocardial infarction models
Bibliographic record
Abstract
Acute myocardial infarction (MI) remains one of the leading causes of death worldwide with no curative therapy available. Stem cell therapies have been gaining interest as a means to repair the cardiac tissue after MI and prevent the onset of heart failure. Many in vivo reports suggest that the use of stem cells is promising, yet clinical trials suggest that the cells fail to integrate into the native tissue, resulting in limited improvements in cardiac function and repair. To battle this limitation, the combination of using stem cells embedded in a bioactive scaffold that promotes cell retention is growing in interest. Yet, a systematic review of the literature on the use of stem cells embedded in bioactive scaffolds for cardiac repair has not yet been performed. In this protocol, we outline a systematic review and meta-analysis of preclinical trials in animal MI models that utilize stem cell-embedded scaffolds for cardiac repair and compare their effects to stem cell-treated animals without the use of a scaffold. We will search the following electronic databases: Cochrane Library, MEDLINE, Embase, PubMed, Scopus and Web of Science, and gray literature: Canadian Agency for Drugs and Technologies in Health and Google Scholar. We will only include randomly controlled preclinical trials that have directly investigated the effects of stem cells embedded in a scaffold for cardiac repair in an animal MI model. Two investigators will independently review each article included in the final analysis. The primary endpoint that will be investigated is left ventricular ejection fraction. Secondary endpoints will include infarct size, end systolic volume, end diastolic volume, fractional shortening and left ventricular wall thickness. Pooled analyses will be conducted using the DerSimonian-Laird random effects and Mantel-Haenszel fixed-effect models. Between-studies heterogeneity will be quantified and determined using the Tau2 and I2 statistics. Publication bias will be assessed using visual inspection of funnel plots and complemented by Begg’s and Egger’s statistical tests. Possible sources of heterogeneity will be assessed using subgroup-meta analysis and meta-regression. To date, the use of scaffolds in myocardial repair has not yet been systematically reviewed. The results of this meta-analysis will aid in determining the efficacy of stem cell-embedded scaffolds for cardiac repair and help bring this therapy to the clinic.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.172 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.037 | 0.021 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".