International Spinal Cord Injury Biobank: A Biorepository and Resource for Translational Research
Bibliographic record
Abstract
Over the past few decades, tremendous advances have been made in our understanding of the biological changes underpinning the devastating impairment of traumatic spinal cord injury (SCI). Much of this scientific research has focused on animal models of SCI, and comparatively little has been done in human SCI, largely because biospecimens from human SCI patients are not readily available. This paucity of scientific enquiry in human SCI represents an important void in the spectrum of translational research, as biological differences between animal models and the human condition need to be considered in the pre-clinical development of therapeutic approaches. The International Spinal Cord Injury Biobank (ISCIB) is a multi-user biorepository with the mission of accelerating therapeutic development in traumatic SCI through improved biological understanding of human injury, and the vision of serving as a global research resource where human SCI biospecimens are shared with researchers around the world. Aligned with internationally recognized best practices, ISCIB's formal governance structure and standard operating procedures have earned it official biobank certification through the Canadian Tissue Repository Network. Herein, we describe the translational research gap that ISCIB is helping to fill; its structure, governance and certification; how data and samples are accrued, processed and stored; and finally, the process through which samples and data are shared with global researchers. The purpose of this paper describing ISCIB is to serve as an introductory guidance document for the wider community of SCI researchers. By helping researchers understand the contents of ISCIB and the process of accessing biospecimens, we seek to further ISCIB's vision as being a resource for human and translational research in SCI, with the ultimate goal of finding disease-modifying therapies for this disabling condition.
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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 | 0.166 | 0.132 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.018 | 0.022 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.020 | 0.011 |
| Open science | 0.008 | 0.022 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.057 | 0.050 |
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".