The challenges of translating stem cells for spinal cord injury and related disorders: what are the barriers and opportunities?
Bibliographic record
Abstract
Stem cell therapies have significant potential to treat spinal cord injury (SCI), but it remains difficult to translate these therapies from 'bench to bedside'. Identifying barriers to translation and understanding how these barriers are viewed by stakeholders in the field of stem cell research are key steps to clinical translation. The Stem Cell Global Blueprint Conference, held in Toronto (ON, Canada) presented a unique opportunity to analyze the perspectives of multiple stakeholders on the future of stem cell therapies for SCI treatment. This article is an analysis of data collected at the conference, including a consensus-building process and pre- and in-conference questionnaires. The authors used these data to assess current perceptions of stem cell research and compared the findings with the literature. The authors identified the major barriers according to a wide range of stakeholders and what strategies they suggested to overcome these obstacles, with the aim of forwarding discussion on stem cell research. It is not a systematic review of the area, but rather a presentation of expert opinion with literature citations to give context and support to their arguments and suggestions. The authors believe that the international SCI community is ready for larger-scale clinical translation, which will require the continued cooperation of all stakeholders in the stem cell and SCI communities.
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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 | 0.037 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 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".