Biospecimen Complexity—the Next Challenge for Cancer Research Biobanks?
Bibliographic record
Abstract
Abstract Purpose: Biospecimens (e.g., tissues, bloods, fluids) are critical for translational cancer research to generate the necessary knowledge to guide implementation of precision medicine. Rising demand and the need for higher quality biospecimens are already evident. Experimental Design: The recent increase in requirement for biospecimen complexity in terms of linked biospecimen types, multiple preservation formats, and longitudinal data was explored by assessing trends in cancer research publications from 2000 to 2014. Results: A PubMed search shows that there has been an increase in both raw numbers and the relative proportion (adjusted for total numbers of articles in each period) of the subgroups of articles typically associated with the use of biospecimens and both dense treatment and/or outcomes data and multiple biospecimen formats. Conclusions: Increasing biospecimen complexity is a largely unrecognized and new pressure on cancer research biobanks. New approaches to cancer biospecimen resources are needed such as the implementation of more efficient and dynamic consent mechanisms, stronger participant involvement in biobank governance, development of requirements for registration of collections, and models to establish stock targets for biobanks. In particular, the latter two approaches would enable funders to establish a better balance between biospecimen supply and research demand, reduce expenditure on duplicate collections, and encourage increased efficiency of biobanks to respond to the research need for more complex cases. This in turn would also enable biobanks to focus more on quality and standardization that are surely factors in the even more important arena of research reproducibility. Clin Cancer Res; 23(4); 894–8. ©2016 AACR.
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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 | 0.303 | 0.494 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.009 | 0.016 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.025 | 0.050 |
| Open science | 0.007 | 0.014 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".