Translational Research in Spinal Cord Injury: A Survey of Opinion from the SCI Community
Bibliographic record
Abstract
Much like our colleagues studying neuroprotection for acute stroke, we in the spinal cord injury (SCI) community have witnessed the preclinical emergence of numerous promising neuroprotective and neuro-regenerative treatments that have then disappointingly failed to demonstrate convincing efficacy in clinical trials. In contrast to the stroke field, the SCI community lacks guidelines to steer the preclinical development of therapies and maximize their chance of success prior to translation into expensive and laborious clinical trials. We conducted a survey of the SCI research community to garner perspectives on the question of what preclinical evidence was required before translating an experimental treatment into clinical trials. The opinions of the 324 respondents about what constitutes necessary preclinical evidence before moving to human SCI trials revealed strong support for the demonstration of efficacy in large-animal models, cervical injury models, and for independent replication of promising results. Marked differences exist between the sentiments of the respondents and the translational experience of our field. A framework for guiding the preclinical development of novel therapies prior to human translation would be helpful for ensuring clinical success. Greater dialogue on this issue is necessary to improve our chances of successfully bringing effective treatments to patients with this devastating injury.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.009 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".