Biobanking in the COVID-19 Era and Beyond: Part 2. A Set of Tool Implementation Case Studies
Bibliographic record
Abstract
The COVID-19 era has brought about a number of novel challenges for the global biobanking community. An array of diverse tools (e.g., standards, best practices, and plans) exists to support quality and fitness-for-purpose in biobank operations. The International Society for Biological and Environmental Repositories (ISBER) COVID-19 Response Task Force has set out to identify needs and gaps in these tools and make recommendations for the next generation of available tools, having closely examined the COVID-19-related challenges. While conducting this work to examine the relationships between tools and biobank adaptability, a subgroup of the task force conducted a parallel effort to develop and describe individual COVID-19 era case studies based on a number of operating biobanks. Each case study presents a different combination of implemented tools. Observations and lessons learned from these case studies are provided, and experiences with tool implementation are discussed. This information is supplemented by data relating to tool usefulness that was obtained through an ISBER survey discussed in a companion article. The knowledge gained from this study will be combined with other task force efforts to make recommendations to better position the biobanking community in their response to future emergencies.
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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 | 0.053 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".