A Grading System To Evaluate Objectively the Strength of Pre-Clinical Data of Acute Neuroprotective Therapies for Clinical Translation in Spinal Cord Injury
Bibliographic record
Abstract
The past three decades have seen an explosion of research interest in spinal cord injury (SCI) and the development of hundreds of potential therapies that have demonstrated some promise in pre-clinical experimental animal models. A growing number of these treatments are seeking to be translated into human clinical trials. Conducting such a clinical trial, however, is extremely costly, not only for the time and money required to execute it, but also for the limited resources that will then no longer be available to evaluate other promising therapies. The decision about what therapies have sufficient pre-clinical evidence of efficacy to justify testing in humans is therefore of utmost importance. Here, we have developed a scoring system for objectively grading the body of pre-clinical literature on neuroprotective treatments for acute SCI. The components of the system include an evaluation of a number of factors that are thought to be important in considering the "robustness" of a therapy's efficacy, including the animal species and injury models that have been used to test it, the time window of efficacy, the types of functional improvements effected by it, and whether efficacy has been independently replicated. The selection of these factors was based on the results of a questionnaire that was performed within the SCI research community. A modified Delphi consensus-building exercise was then conducted with experts in pre-clinical SCI research to refine the criteria and decide upon how to score them. Finally, the grading system was applied to a series of potential neuroprotective treatments for acute SCI. This represents a systematic approach to developing an objective method of evaluating the extent to which the pre-clinical literature supports the translation of a particular experimental treatment into human trials.
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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 | 0.063 | 0.110 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.016 | 0.008 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".