Canadian Tissue Repository Network Biobank Certification Program: Update and Review of the Program from 2011 to 2018
Bibliographic record
Abstract
The Canadian Tissue Repository Network (CTRNet) Biobank Certification Program was first launched in 2011 to foster translational research through improved access to high quality biospecimens. This was accomplished by creating and providing biobank education and through the establishment and deployment of common standards to harmonize biospecimen quality and approaches to governance. The CTRNet program comprises registration and certification steps as two linked phases. In the two-step registration phase, the biobank is registered into the system, and an individual completes an overview educational module. In the subsequent certification phase, biobanks undergo a seven-step process, including inviting team members, assigning and completing relevant education modules, uploading documents, and undergoing a documentation audit. As of June 2018, there were 251 biobanks engaged in the CTRNet program, 193 had completed registration, and 40 were fully certified. Over 3/4 of these biobanks completed registration within a week and over 1/3 completed certification within a month. Among registered biobanks, 163 were associated with North American institutions, while 30 were from other international locations, including Australia, Europe, and Asia. The CTRNet program enables biobanks to adopt standards with a flexible approach to accommodate different types of biobanks and a measured investment of effort, creating the foundation for increased access to high quality biospecimens.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".