Vignettes to Illustrate the Value of Tumor Biobanks in Cancer Research in Canada
Bibliographic record
Abstract
Background: Tumor biobanks are a common research infrastructure. As a collection of biospecimens and annotated data collected to support a multitude of research projects, biobanks facilitate access to materials that are the critical fuel for the generation of data in up to 40% of cancer research publications. However, quantifying how to measure biobanks' impact and their value on the field of cancer research discoveries and findings, has not been well elucidated. Methods: We have used a qualitative case study approach to illustrate the impact of tumor biobanks. We assessed the impact of three research studies published between 2010 and 2012 that required easily accessible “classic” biobanks. Each study utilized preassembled collections of tumor biospecimens with associated patient outcomes data at the outset of the research project. We compared the resulting journal impact factor, altmetric and field-weighted citation impact factor scores for each article to a set of six “benchmark” articles that represent cancer research and treatment discoveries from the same time period and two sentinel scientific discovery articles. Results: We developed a value model using a literature search and design-thinking methodologies to illustrate the contributions of these “classic” model biobanks to these research studies. Assessment of the three example articles supported by biobanks demonstrates that the output can have impact that is comparable to the impact of a set of benchmark articles describing milestones in the field of cancer research and cancer care. Conclusions: These case studies illustrate the value of the sustained investment of funds, planning, time, and effort on the part of the biobanks before the conduct of the research study to be able to ultimately support high-value research. The “value” model will enable further discussion around impact and may be useful in better delineating qualitative metrics of biobank value in the future.
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How this classification was reachedexpand
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".