Biospecimen User Fees: Global Feedback on a Calculator Tool
Bibliographic record
Abstract
The notion of attributing user fees to researchers for biospecimens provided by biobanks has been discussed frequently in the literature. However, the considerations around how to attribute the cost for these biospecimens and data have, until recently, not been well described. Common across most biobank disciplines are similar factors that influence user fees such as capital and operating costs, internal and external demand, and market competition. A biospecimen user fee calculator tool developed by CTRNet, a tumor biobank network, was published in 2014 and is accessible online at www.biobanking.org . The next year a survey was launched that tested the applicability of this user fee tool among a global health research biobank user base, including both cancer and noncancer biobanking. Participants were first asked to estimate user fee pricing for three hypothetical user scenarios based on their biobanking experience (estimated pricing) and then to calculate fees for the same scenarios using the calculator tool (calculated pricing). Results demonstrated variation in estimated pricing that was reduced by calculated pricing. These results are similar to those found in a similar previous study restricted to a group of Canadian tumor biobanks. We conclude that the use of a biospecimen user fee calculator contributes to reduced variation of user fees and for biobank groups (e.g., biobank networks), could become an important part of a harmonization strategy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".