Translational Research in Pediatrics: Tissue Sampling and Biobanking
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.010 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it