Generating a Comprehensive Set of Standard Operating Procedures for a Biorepository Network—The CTRNet Experience
Bibliographic record
Abstract
Despite the integral role of biorepositories in fueling translational research and the advancement of medicine, there are significant gaps in harmonization of biobanking practices, resulting in variable biospecimen collection, storage, and processing. This significantly impacts accurate downstream analysis and, in particular, creates a problem for biorepository networks or consortia. The Canadian Tumour Repository Network (CTRNet; www.ctrnet.ca ) is a consortium of Canadian tumor biorepositories that aims to enhance biobanking capacity and quality through standardization. To minimize the issue of variable biobanking practices throughout its network, CTRNet has developed and maintained a comprehensive set of 45 standard operating procedures (SOPs). There were four key elements to the CTRNet SOP development process: 1) an SOP development team was formed from members across CTRNet to co-produce each SOP; 2) a principal author was appointed with responsibility for overall coordination of the SOP development process; 3) the CTRNet Management Committee (composed of principal investigators for each member biorepository) reviewed/revised each SOP completed by the development team; and 4) external expert reviewers provided feedback and recommendations on each SOP. Once final Management Committee approval was obtained, the ratified SOP was published on the CTRNet website for public access. Since the SOPs were first published on the CTRNet website (June 2008), there have been approximately 15,000 downloads of one or more CTRNet SOPs/Policies by users from over 60 countries. In accordance with biobanking best practices, CTRNet performs an exhaustive review of its SOPs at set intervals, to coincide with each granting cycle. The last revision was completed in May 2012.
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 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 | 0.253 | 0.166 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.011 | 0.015 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 0.015 |
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".