Effect of stem cell transplantation on patients with ischemic heart failure: a systematic review and meta-analysis of randomized controlled trials
Bibliographic record
Abstract
Abstract Stem cell transplantation (SCT) has become a promising way to treat ischemic heart failure (IHF). We performed a large-scale meta-analysis of randomized clinical trials to investigate the efficacy and safety of SCT in IHF patients. Randomized controlled trials (RCTs) involving stem cell transplantation for the treatment of IHF were identified by searching the PubMed, EMBASE, SpringerLink, Web of Science, and Cochrane Systematic Review databases as well as from reviews and the reference lists of relevant articles. Fourteen eligible randomized controlled trials were included in this study, for a total of 669 IHF patients, of which 380 patients were treated with SCT. The weighted mean difference (WMD) was calculated for changes in the New York Heart Association (NYHA) class, left ventricular ejection fraction (LVEF), left ventricular end-diastolic and end-systolic volumes (LVEDV and LVESV), and Canadian Cardiovascular Society (CCS) angina grade using a fixed effects model, while relative risk (RR) was used for mortality. Compared with the control group, SCT significantly lowered the NYHA class (MD = − 0.73, 95% CI − 1.32 to − 0.14, P < 0.05), LVESV (MD = − 14.80, 95% CI − 20.88 to − 8.73, P < 0.05), and CCS grade (MD = − 0.81, 95% CI − 1.45 to − 0.17, P < 0.05). Additionally, SCT increased LVEF (MD = 6.55, 95% CI 5.93 to 7.16, P < 0.05). However, LVEDV (MD = − 0.33, 95% CI − 1.09 to 0.44, P > 0.05) and mortality (RR = 0.86, 95% CI 0.45 to 1.66, P > 0.05) did not differ between the two groups. This meta-analysis suggests that SCT may contribute to the improvement of LVEF, as well as the reduction of the NYHA class, CCS grade, and LVESV. In addition, SCT does not affect mortality.
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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 | 0.017 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.040 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".