P260 A Model For Bridging The Translational Valley Of Death In Spinal Cord Injury
Bibliographic record
Abstract
Background Despite the amount of funding that supports basic research, few research discoveries achieve their potential. The transition from bench-to-bedside research is so fraught with obstacles that it is referred to as the “valley of death”. Objective The Rick Hansen Institute (RHI) developed a unique Praxis Model for translational research in the field of spinal cord injury (SCI). At RHI this means bringing knowledge into action; to improve healthcare outcomes for people with SCI and decrease the financial impact on the healthcare system. Methods The research continuum begins with discovery science which feeds into the knowledge cycle, continues with the acceptance and uptake into the treatment of spinal cord injuries. The core activity within the Praxis Model is a knowledge cycle that consists of a four-phased strategy: 1) Environmental scan, 2) Knowledge generation and synthesis, 3) Knowledge validation, and 4) Implementation. Results RHI has participated and supported over 60 studies since 2007 and engaged researchers from nine countries, 46 academic institutions and various accreditation and professional associations. Currently, the model is being independently evaluated to determine strengths and limitations. Examples of RHI initiatives using the Praxis Model and results of the evaluation will be presented. Discussion RHI has developed an innovative solution to move knowledge into action. The Praxis Model strives to lead collaboration across the global SCI community by providing funding, infrastructure, strategic partnerships, governance and a network. Implications Lessons learned in developing the Praxis Model may assist other organisations dealing with similar translational research challenges.
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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 | 0.075 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.009 | 0.048 |
| Scholarly communication | 0.022 | 0.024 |
| Open science | 0.005 | 0.025 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.014 | 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".