Benefits and obstacles to cell therapy in neonates: The INCuBAToR (Innovative Neonatal Cellular Therapy for Bronchopulmonary Dysplasia: Accelerating Translation of Research)
Bibliographic record
Abstract
Cell-based therapies hold promise to substantially curb complications from extreme preterm birth, the main cause of death in children below the age of 5 years. Exciting preclinical studies in experimental neonatal lung injury have provided the impetus for the initiation of early phase clinical trials in extreme preterm infants at risk of developing bronchopulmonary dysplasia. Clinical translation of promising therapies, however, is slow and often fails. In the adult population, results of clinical trials so far have not matched the enticing preclinical data. The neonatal field has experienced many hard-earned lessons with the implementation of oxygen therapy or postnatal steroids. Here we briefly summarize the preclinical data that have permitted the initiation of early phase clinical trials of cell-based therapies in extreme preterm infants and describe the INCuBAToR concept (Innovative Neonatal Cellular Therapy for Bronchopulmonary Dysplasia: Accelerating Translation of Research), an evidence-based approach to mitigate the risk of translating advanced therapies into this vulnerable patient population. The INCuBAToR addresses several of the shortcomings at the preclinical and the clinical stage that usually contribute to the failure of clinical translation through (a) systematic reviews of preclinical and clinical studies, (b) integrated knowledge transfer through engaging important stakeholders early on, (c) early economic evaluation to determine if a novel therapy is viable, and (d) retrospective and prospective studies to define and test ideal eligibility criteria to optimize clinical trial design. The INCuBAToR concept can be applied to any novel therapy in order to enhance the likelihood of success of clinical translation in a timely, transparent, rigorous, and evidence-based fashion.
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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 | 0.038 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".