Cellular Treatments for Spinal Cord Injury: The Time is Right for Clinical Trials
Bibliographic record
Abstract
More than 1 million people in the United States live with a spinal cord injury (SCI). Despite medical advances, many patients with SCIs still experience substantial neurological disability, with loss of motor, sensory, and autonomic function. Cell therapy is ideally suited to address the multifactorial nature of the secondary events following SCI. Remarkable advances in our understanding of the pathophysiology of SCI, structural and functional magnetic resonance imaging, image-guided micro-neurosurgical techniques, and transplantable cell biology have enabled the use of cell-based regenerative techniques in the clinic. It is important to note that there are more than a dozen recently completed, ongoing, or recruiting cell therapy clinical trials for SCI that reflect the views of many key stakeholders. The field of regenerative neuroscience has reached a stage in which the clinical trials are scientifically and ethically justified. Although experimental models and analysis methods and techniques continue to evolve, no model will completely replicate the human condition. It is recognized that more work with cervical models of contusive/compressive SCI are required in parallel with clinical trials. It is also important that the clinical translation of advances made through well-established and validated experimental approaches in animal models move forward to meet the compelling needs of individuals with SCI and to advance the field of regenerative neuroscience. However, it is imperative that such efforts at translation be done in the most rigorous and informed fashion to determine safety and possible efficacy, and to provide key information to clinicians and basic scientists, which will allow improvements in regenerative techniques and the validation and refinement of existing preclinical animal models and research approaches. The field of regenerative neuroscience should not be stalled at the animal model stage, but instead the clinical trials need to be focused, safe, and ethical, backed up by a robust, translationally relevant preclinical research strategy.
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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 | 0.047 | 0.072 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.018 | 0.023 |
| Insufficient payload (model declined to judge) | 0.026 | 0.009 |
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".