An Open Access Collaborative Global Virtual Biorepository System (VBS): A Delphi Consensus
Bibliographic record
Abstract
Abstract Background The re-emergence of Zika virus, the novel coronavirus, and new influenza strains highlighted the urgent need for an organized forum to access specimens for advancing research, diagnostics and vaccine development. A Virtual Biorepository System (VBS) was proposed to connect communities and samples, addressing specimen-sharing challenges while promoting inclusion and equity. Aim and Objectives To enable equitable contribution, sharing, and access to specimens while addressing the complexities involved. The objective is to build stakeholder consensus on prioritized actions, tools, and responsibilities to establish a functional and practical VBS. Methods A Delphi process was conducted online to gather stakeholder input and develop a prioritized action plan for the VBS. Respondents were recruited using snowball sampling, and questions were informed by participatory workshops, feedback, outreach efforts, and discussions to identify gaps and best practices. The Delphi process consisted of two rounds: an initial ranking informed by stakeholder inputs and a second round of refining priorities based on panel feedback. Consensus threshold was defined as ≥70% of responses rating an item ≥4 and excluded if an item had ≤15% response or was rated < 2 on a 1–5 Likert scale. Advisory board discussions supplemented the virtual process to capture nuances, while qualitative data were analyzed using participatory action research methods. Findings The Delphi process identified critical gaps and priorities in biorepository systems. Consensus was reached for 22 out of 30 items. Items 20 of 26 in Round-1, with 2 of 4 additional items in Round-2 where consensus was reached on 2 items addressing benefit-sharing challenges (73%) and prioritizing whole blood, plasma, and serum samples (95%). Respondents overwhelmingly supported a hybrid VBS model combining federated and centralized systems (83%) cost recovery for services (100%) Some respondents suggested an equitable fee structure using a sliding scale to subsidize capacity building in low-and middle-income countries (95%). Conclusions Respondents strongly supported a VBS to enable equitable access to specimens, services, and benefits. They emphasized the need for clarity on benefit-sharing, regulatory, and ethical frameworks to ensure effective implementation and global inclusivity. A fair and equivalent system to enhance participation and operational continuity is being discussed.
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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 | 0.176 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.005 | 0.024 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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 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".