MétaCan
Menu
Back to cohort
Record W2326013668 · doi:10.1089/bio.2014.0051

Biobank Bootstrapping: Is Biobank Sustainability Possible Through Cost Recovery?

2014· article· en· W2326013668 on OpenAlexaffabout
Monique Albert, John M.S. Bartlett, Randal N. Johnston, Brent Schacter, Peter H. Watson

Bibliographic record

VenueBiopreservation and Biobanking · 2014
Typearticle
Languageen
FieldMedicine
TopicEthics in Clinical Research
Canadian institutionsOntario Institute for Cancer Research
Fundersnot available
KeywordsBiobankSustainabilityDistribution (mathematics)BusinessComputer scienceRisk analysis (engineering)Actuarial scienceBioinformatics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.035
metaresearch head score (Gemma)0.112
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Incentives · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.965
Threshold uncertainty score0.183

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0350.112
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.004
Science and technology studies0.0010.003
Scholarly communication0.0060.005
Open science0.0030.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.344
GPT teacher head0.510
Teacher spread0.166 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designTheoretical or conceptual
DomainIncentives
GenreEmpirical

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".

Quick stats

Citations52
Published2014
Admission routes2
Has abstractyes

Explore more

Same venueBiopreservation and BiobankingSame topicEthics in Clinical ResearchFrench-language works237,207