Biobank Bootstrapping: Is Biobank Sustainability Possible Through Cost Recovery?
Bibliographic record
Abstract
BACKGROUND: The pre-eminent goal of biobanks is to accelerate scientific discovery and support improvements in healthcare through the supply of high quality biospecimens to enable excellent science. Despite the need for retrospective future-proofed cancer repositories, they are presented with significant fiscal challenges. While it was once thought that biobanks could recover most, if not all, operational costs through distribution fees, biobanks have been consistently unable to fully realize this dream. METHODS: Using data from three mature Canadian cancer biobanks, common attributes and assumptions related to cost recovery were evaluated. The values were entered into a simple financial model to determine the cost recovery potential for biobanks. RESULTS: Over a 5-year period analyzed, aliquots from almost 40% (8990) of 23055 cases collected have been distributed in whole or in part to researchers. The financial modeling demonstrates that, based on values derived from the real life experiences of three major Canadian biobanks, full cost recovery through distribution is not feasible. A more realistic, experience based, expectation of cost recovery from distribution fees is in the range of 5%-25%, and this range is lower if only academic research is supported as opposed to also supporting industry researchers. CONCLUSIONS: Biobanks are expensive and, to mitigate costs, are frequently challenged to operate under "self-sustainable" financial models. However, the only possible route to self-sustainability through distribution fees in today's market would require an almost exclusive targeting of commercial researchers and, even then, evidence suggests this is an impossible goal to attain. Support for biobanks should recognize that they exist to further development of personalized treatments and diagnostics essential for precision medicine. For biobanks to continue to achieve this goal, pro bono publicum, funders need to be aware of the full funding requirements of biobanks and create appropriate funding streams.
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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 | 0.035 | 0.112 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".