A comprehensive meta‐analysis of stem cell therapy for chronic angina
Bibliographic record
Abstract
BACKGROUND: A substantial proportion of patients with coronary artery disease do not achieve complete revascularization and continue to experience refractory angina despite optimal medical therapy. Recently, stem cell therapy has emerged as a potential therapeutic option for these patients. However, findings of individual trials have been scrutinized because of their small sample sizes and lack of statistical power. Therefore, we conducted an updated comprehensive meta-analysis of available randomized controlled trials (RCTs) with the largest sample size ever reported on this subject. HYPOTHESIS: In patients with chronic angina stem cell therapy improves clinical outcomes. METHODS: Scientific databases and websites were searched for RCTs. Data were independently collected by 2 investigators, and disagreements were resolved by consensus. Data from 10 trials including 658 patients were analyzed. RESULTS: Stem cell therapy improved Canadian Cardiovascular Society angina class (risk ratio: 1.53, 95% CI: 1.09 to 2.15, P = 0.013), exercise capacity (standardized mean difference [SMD]: 0.56, 95% CI: 0.23 to 0.88, P = 0.001), and left ventricular ejection fraction (SMD: 0.63, 95% CI: 0.27 to 1.00, P = 0.001) compared with placebo. It also decreased anginal episodes (SMD: -1.21, 95% CI: -2.40 to -0.02, P = 0.045) and myocardial perfusion defects (SMD: -0.70, 95% CI: -1.11 to -0.29, P = 0.001). However, no improvements in all-cause mortality were observed after a relatively short follow-up. CONCLUSIONS: In patients with chronic angina on optimal medical therapy, stem cell therapy improves symptoms, exercise capacity, and left ventricular ejection fraction. These findings warrant confirmation using larger trials.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.045 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".