A Model to Estimate Frozen Tissue Collection Targets in Biobanks to Support Cancer Research
Bibliographic record
Abstract
Human biospecimens are used in 40% of cancer research publications. Tumor biobanks are an important source for these biospecimens and support both prospective and retrospective research studies. Supporting retrospective research requires tumor tissue biobanks to accrue an adequate inventory, or stock, of cases comprising tumor biospecimens and associated treatment and outcomes data. We propose a model to establish appropriate targets for stocks of frozen tissue biospecimens in tumor biobanks, sufficient to support cancer research needs. Our model considers national levels of investment in academic cancer research relative to research use of cases described in publication output, and scales this to the local context of the BC Cancer Agency Tumour Tissue Repository (TTR) as an example. Adjustment factors are then applied to correct for the primary intended user base of the biobank, as well as variables intrinsic to all biobanking operations and case collection. On this basis we estimate a current target stock for the TTR of approximately 4500 cases. Local research demand derived from case release data can then be applied to fine-tune accrual targets and refine the biobank's relative portfolio of cases from different tumor sites. We recognize that current targets will need regular remodeling as research demands change over time and that our initial model has some limitations related to the need to extrapolate from available research and biobank utilization data, and does not incorporate biospecimen/case contributions within the context of a network. However, we believe the lack of models to estimate inventory targets for tumor biobanks and to better balance research demand with biospecimen supply, contributes to the hesitation of funders to provide support, and also the problems of sustainability faced by many biobanks. Creating tangible inventory targets will improve biobank efficiency, sustainability, and may also encourage increased and stable funding.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".