Targeting Translational Successes through CANSORT-SCI: Using Pet Dogs To Identify Effective Treatments for Spinal Cord Injury
Bibliographic record
Abstract
Translation of therapeutic interventions for spinal cord injury (SCI) from laboratory to clinic has been historically challenging, highlighting the need for robust models of injury that more closely mirror the human condition. The high prevalence of acute, naturally occurring SCI in pet dogs provides a unique opportunity to evaluate expeditiously promising interventions in a population of animals that receive diagnoses and treatment clinically in a manner similar to persons with SCI, while adhering to National Institutes of Health guidelines for scientific rigor and transparent reporting. In addition, pet dogs with chronic paralysis are often maintained long-term by their owners, offering a similarly unique population for study of chronic SCI. Despite this, only a small number of studies have used the clinical dog model of SCI. The Canine Spinal Cord Injury Consortium (CANSORT-SCI) was recently established by a group of veterinarians and basic science researchers to promote the value of the canine clinical model of SCI. The CANSORT-SCI group held an inaugural meeting November 20 and 21, 2015 to evaluate opportunities and challenges to the use of pet dogs in SCI research. Key challenges identified included lack of familiarity with the model among nonveterinary scientists and questions about how and where in the translational process the canine clinical model would be most valuable. In light of these, we review the natural history, outcome, and available assessment tools associated with canine clinical SCI with emphasis on their relevance to human SCI and the translational process.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".