Connecting Researchers and Clinicians Before Connecting the Injured Spinal Cord
Bibliographic record
Abstract
Background Translating research findings from animal models of spinal cord injury (SCI) to humans is a challenging enterprise. It is likely that differences in the use of common terms contribute to this. Objectives The purpose of this study was to identify how scientists and clinicians define terms used across the research and clinical care continuum. Methods We utilized the Delphi technique to develop consensus on the opinions of experts (defined as researchers and/or clinicians working in the field of SCI) through a series of structured, iterative surveys. A focus group of stakeholders developed the terms on the initial survey. Results were used to create definitions and formulate questions for a second and third survey. Results Survey 1 yielded one definition for eight terms and multiple definitions for six terms in addition to three new terms that respondents believed should be defined. In Survey 2, definitions for eight terms reached at least 80% agreement: anatomically complete spinal cord injury, functionally complete spinal cord injury, neuromodulation, physical exercise, physical rehabilitation, plasticity, task specificity, and training intensity. Consensus was not reached for six terms. In Survey 3, definitions for seven additional terms reached at least 80% agreement: recovery, repair, compensation, regeneration, physical function, physiological function, and chronic . There were three terms that did not reach agreement after the three rounds: acute, translational research , and sprouting . Conclusion We found that different terminology contributes to the gap between preclinical and clinical research and clinical application. This suggests that increased communication among different disciplines could be a way to advance the field.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.220 | 0.364 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.014 | 0.018 |
| Scholarly communication | 0.019 | 0.030 |
| Open science | 0.004 | 0.030 |
| Research integrity | 0.017 | 0.016 |
| Insufficient payload (model declined to judge) | 0.017 | 0.006 |
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".