Translational Research in Pediatrics: Tissue Sampling and Biobanking
Bibliographic record
Abstract
Translational research is expanding and has become a focus of National Research funding agencies, touted as the primary avenue to improve health care practice. The use of human tissues for research on disease etiology is a pillar of translational research, particularly with innovations in research technologies to investigate the building blocks of disease. In pediatrics, translational research using human tissues has been hindered by the many practical and ethical considerations associated with tissue procurement from children and also by a limited population base for study, by the increasing complexities in conducting clinical research, and by a lack of dedicated child-health research funding. Given these obstacles, pediatric translational research can be enhanced by developing strategic and efficient biobanks that will provide scientists with quality tissue specimens to render accurate and reproducible research results. Indeed, tissue sampling and biobanking within pediatric academic settings has potential to impact child health by promoting bidirectional interaction between clinicians and scientists, helping to maximize research productivity, and providing a competitive edge for attracting and maintaining high-quality personnel. The authors of this review outline key issues and practical solutions to optimize pediatric tissue sampling and biobanking for translational research, activities that will ultimately reduce the burden of childhood disease.
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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 | 0.019 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".