Opinions on the Preclinical Evaluation of Novel Therapies for Spinal Cord Injury: A Comparison between Researchers and Spinal Cord-Injured Individuals
Bibliographic record
Abstract
We previously conducted a survey to gather the opinions and perspectives of scientific and clinical researchers on what levels of preclinical evidence were needed to justify translating a promising neuroprotective or neuroregenerative therapy in spinal cord injury (SCI) into a human clinical trial (Kwon et al., 2010 ). Here we conducted an analogous survey of individuals living with SCI in which we gathered their expectations for the levels of preclinical evidence achieved by researchers in substantiating the neuroprotective and neuroregenerative therapies being offered to them in clinical trials. In total, 214 individuals with SCI completed the survey, and their responses were compared to the responses of the 235 scientists and clinicians who completed our previous survey. SCI individuals were more likely than SCI researchers to opine that demonstrating efficacy and safety in rodent models of SCI alone is sufficient to proceed with clinical trials. However, SCI individuals also reported strong support for large animal and primate model studies, and in the case of the latter, were actually more in agreement for the need for primate studies than researchers. SCI individuals also reported strong support for independent replication studies. In general, individuals with SCI had high expectations for the levels of preclinical evidence required to justify translating novel therapies into clinical trials. These expectations should be considered in the decisions to translate specific experimental therapies for SCI.
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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 | 0.074 | 0.183 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".