A Systematic Review of Cellular Transplantation Therapies for Spinal Cord Injury
Bibliographic record
Abstract
Cell transplantation therapies have become a major focus in pre-clinical research as a promising strategy for the treatment of spinal cord injury (SCI). In this article, we systematically review the available pre-clinical literature on the most commonly used cell types in order to assess the body of evidence that may support their translation to human SCI patients. These cell types include Schwann cells, olfactory ensheathing glial cells, embryonic and adult neural stem/progenitor cells, fate-restricted neural/glial precursor cells, and bone-marrow stromal cells. Studies were included for review only if they described the transplantation of the cell substrate into an in-vivo model of traumatic SCI, induced either bluntly or sharply. Using these inclusion criteria, 162 studies were identified and reviewed in detail, emphasizing their behavioral effects (although not limiting the scope of the discussion to behavioral effects alone). Significant differences between cells of the same "type" exist based on the species and age of donor, as well as culture conditions and mode of delivery. Many of these studies used cell transplantations in combination with other strategies. The systematic review makes it very apparent that cells derived from rodent sources have been the most extensively studied, while only 19 studies reported the transplantation of human cells, nine of which utilized bone-marrow stromal cells. Similarly, the vast majority of studies have been conducted in rodent models of injury, and few studies have investigated cell transplantation in larger mammals or primates. With respect to the timing of intervention, nearly all of the studies reviewed were conducted with transplantations occurring subacutely and acutely, while chronic treatments were rare and often failed to yield functional benefits.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".