Challenges in Translating Regenerative Therapies for Spinal Cord Injury
Bibliographic record
Abstract
Regenerating the injured spinal cord is a substantial challenge with many obstacles that need to be overcome to achieve robust functional benefits. This abundance of hurdles can partly explain the limited success when applying regenerative intervention treatments in animal models and/or people. In this article, we elaborate on a few of these obstacles, starting with the applicability of animal models and how they compare to the clinical setting. We then discuss the requirement for combinatorial interventions and the associated problems in experimental design, including the addition of rehabilitative training. The article expands on differences in lesion sizes and locations between humans and common animal models, and how this difference can determine the success or failure of an intervention. An additional and frequently overlooked problem in the translation of interventions that applies beyond the field of neuroregeneration is the reporting bias and the lack of transparency in reporting findings. New data mandates are tackling this problem and will eventually result in a more balanced view of the field. Finally, we will discuss strategies to negotiate the challenging course of successful translation to facilitate successful translation of regeneration promoting interventions.
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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 | 0.456 | 0.589 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.018 | 0.019 |
| Open science | 0.008 | 0.015 |
| Research integrity | 0.009 | 0.018 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".