Biobanking for better healthcare
Bibliographic record
Abstract
Translational cancer research is highly dependent of large series of cases including high quality samples and their associated data. Comprehensive Cancer Centers should be involved in networks to enable large-scale multi-center research projects between the centers [Ringborg, U., de Valeriola, D., van Harten, W., Llombart-Bosch, A., Lombardo, C., Nilsson, K., Philip, T., Pierotti, M.A., Riegman, P., Saghatchian, M., Storme, G., Tursz, T., Verellen, D, 2008. Improvement of European translational cancer research. Collaboration between comprehensive cancer centers. Tumori 94, 143-146.]. Combating cancer knows many frontiers. Research is needed for prevention as well as better care for those who have acquired the disease. This implies that human samples for cancer research need to be sourced from distinct forms of biobanking. An easier access to these samples for the scientific community is considered as the main bottleneck for research for health, and biobanks are the most adequate site to try to resolve this issue [Ozols, R.F., Herbst, R.S., Colson, Y.L., Gralow, J., Bonner, J., Curran Jr., W.J., Eisenberg, B.L., Ganz, P.A., Kramer, B.S., Kris, M.G., Markman, M., Mayer, R.J., Raghavan, D., Reaman, G.H., Sawaya, R., Schilsky, R.L., Schuchter, L.M., Sweetenham, J.W., Vahdat, L.T., Winn, R.J., and the American Society of Clinical Oncology, 2007. Clinical cancer advances 2006: major research advances in cancer treatment, prevention, and screening: a report from the American Society of Clinical Oncology. J. Clin. Oncol. 25, 146-162.]. However, biobanks should not be considered a static activity. On the contrary, biobanking is a young discipline [Morente, M.M., Fernandez, P.L., de Alava, E. Biobanking: old activity or young discipline? Semin. Diagn. Pathol., in press.], which need continuously evolve according to the permanent development of new techniques and new scientific goals. To accomplish current requirements of the scientific community biobanks need to face some essential challenges including an appropriate design, harmonized and more suitable procedures, and sustainability, all of them in the framework of their ethic, legal and social dimensions. This review therefore presents an overview on these issues, based on the works and discussions of the Marble Arch International Working Group on Biobanking for Biomedical Research, integrated by experts in biobanking from five continents.
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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 | 0.036 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.015 | 0.022 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.061 | 0.049 |
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".