Are all stem cells equal? Systematic review, evidence map, and meta-analyses of preclinical stem cell-based therapies for bronchopulmonary dysplasia
Bibliographic record
Abstract
Abstract Regenerative stem cell-based therapies for bronchopulmonary dysplasia (BPD), the most common preterm birth complication, demonstrate promise in animals. Failure to objectively appraise available preclinical data and identify knowledge gaps could jeopardize clinical translation. We performed a systematic review and network meta-analysis (NMA) of preclinical studies testing cell-based therapies in experimental neonatal lung injury. Fifty-three studies assessing 15 different cell-based therapies were identified: 35 studied the effects of mesenchymal stromal cells (MSCs) almost exclusively in hyperoxic rodent models of BPD. Exploratory NMAs, for select outcomes, suggest that MSCs are the most effective therapy. Although a broad range of promising cell-based therapies has been assessed, few head-to-head comparisons and unclear risk of bias exists. Successful clinical translation of cell-based therapies demands robust preclinical experimental design with appropriately blinded, randomized, and statistically powered studies, based on biological plausibility for a given cell product, in standardized models and endpoints with transparent reporting. Significance statement Bronchopulmonary dysplasia (BPD), a chronic lung disease that follows ventilator and oxygen therapy for acute respiratory failure after premature birth, is the most frequent complication of extreme prematurity. BPD is associated with long-term respiratory and neurological consequences reaching into adulthood and currently lacks effective therapy. Regenerative cell-based therapy, demonstrating therapeutic benefit in experimental neonatal lung injury, has now spawned clinical trials. Yet there has been no systematic review to assess the extent of current evidence regarding safety and efficacy of cell-based therapies in preclinical BPD and identify gaps that could jeopardize successful clinical translation.
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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 | 0.028 | 0.091 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.026 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".