The Importance of Biobanking in Cancer Research
Bibliographic record
Abstract
BACKGROUND: Establishing the importance of biobanking in cancer research is important for research funders and for planning health research infrastructure. This study delineates the importance of biobanking to the cancer research landscape in Canada and relative to other forms of health research infrastructure. METHODS: The Cancer Research Society (CRS) is a Canadian organization with a broad mission and national portfolio that funds studies across the spectrum of cancer research. We selected all 35 investigators who received CRS grants in the 2010/11 competition and then analyzed their publications from 2010 to 2014. Articles were categorized by overall research area, acknowledged source of funding, specific scientific focus, and the presence of any data that involved an 'indicator' (human biospecimens, cell lines, animal models, advanced microscopy, flow cell sorters, and next generation sequencing) of dependence on different kinds of health research infrastructures. Publications involving biobanking and utilizing biospecimens were further classified by biospecimen provenance and type of biospecimen used. RESULTS: These investigators generated 502 (from a total of 749) papers that were related to the field of cancer research. Amongst 445 papers that contained primary data, we found no significant differences between CRS funded and 'other funded' papers in terms of biospecimen use, which occurred in 38% of articles. Overall biospecimens were mostly obtained directly from patients (17%), or indirectly from biorepositories (31%) and hospitals (46%). The proportions of studies using other tools was as follows: 54% cell lines, 32% animal models, 14% advanced microscopy, 14% flow sorters, and 8% next generation sequencing. The spectrum of research was very similar to the overall profile of cancer research in Canada in 2010. CONCLUSIONS: This study suggests that biorepositories that coordinate the activity of biobanking rank amongst the most important of established health research infrastructures as contributors to research publications.
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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 | 0.011 | 0.009 |
| 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".