Bibliographic record
Abstract
The past 20 years has seen the emergence of many exciting and promising experimental therapeutic strategies to promote regeneration of the injured spinal cord in laboratory animals. A greater understanding of the pathophysiologic mechanisms that contribute to the initial and secondary cord injury may facilitate the development of neuroprotective strategies that preserve axonal function and prevent apoptotic cell death, thus optimizing neurologic function. Neurotrophic factors have been used to augment the poor intrinsic regenerative capacity of central nervous system neurons, and the need for sophisticated delivery of such trophic agents has stimulated the application of gene therapy in this context. In addition to augmenting the neuronal capacity to regenerate axons, many researchers are developing strategies to overcome the inhibitory environment into which these axons must grow. Characterizing the inhibitory elements of the glial scar at the site of injury and of myelin in the distal tracts is therefore a focus of intense scientific interest. To this effect, a number of strategies have also been developed to bridge the injury site and facilitate axonal growth across the lesion with a variety of cellular substrates. These include fetal tissue transplants, stem cells, Schwann cells, and olfactory ensheathing cells. With the collaboration of basic scientists and clinicians, it is hoped that these experimental strategies coupled with a greater understanding of the neurobiology of spinal cord injury will be translatable to the clinical setting in the near future.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".