Assessment of safety and efficacy of mesenchymal stromal cell therapy in preclinical models of acute myocardial infarction: a systematic review protocol
Bibliographic record
Abstract
BACKGROUND: Despite advances in treatment, acute myocardial infarction (MI) is still associated with significant morbidity and mortality, especially in patients with extensive damage and scar formation. Based on some promising preclinical studies, there is interest in the use of mesenchymal stromal cells (MSCs) to promote cardiac repair after acute MI. However, there is a need for a systematic review of this evidence to summarize the efficacy and safety of MSCs in preclinical models of MI. This will better inform the translation of MSC therapy for acute MI and guide the design of a future clinical trial. METHODS/DESIGN: A systematic literature search of MEDLINE, Embase, and BIOSIS Previews will be conducted. We will identify comparative preclinical studies (randomized and non-randomized) of myocardial infarction that include animals given MSC therapy versus a vehicle/placebo. The primary outcome will be left ventricular ejection fraction. Secondary and tertiary outcomes will include death, infarct size, measures of cardiac function, biochemical outcomes, and MSC retention and differentiation. Risk of bias will be assessed using the Cochrane Risk of Bias Tool. Subgroup analyses will be performed to measure how various sources of preclinical study heterogeneity affect the direction and magnitude of the primary outcome. We will meta-analyze data using inverse variance random effects modeling. DISCUSSION: This systematic review of preclinical evidence will provide a summary of the efficacy and safety of MSCs in animal models of MI. The results will help determine whether sufficient evidence exists to conduct a clinical trial in humans and inform its design.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.051 | 0.067 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.019 | 0.021 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.029 | 0.004 |
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".