Considerations and recommendations for selection and utilization of upper extremity clinical outcome assessments in human spinal cord injury trials
Bibliographic record
Abstract
STUDY DESIGN: This is a focused review article. OBJECTIVES: This review presents important features of clinical outcomes assessments (COAs) in human spinal cord injury research. Considerations for COAs by trial phase and International Classification of Functioning, Disability and Health are presented as well as strengths and recommendations for upper extremity COAs for research. Clinical trial tools and designs to address recruitment challenges are identified. METHODS: The methods include a summary of topics discussed during a two-day workshop, conceptual discussion of upper extremity COAs and additional focused literature review. RESULTS: COAs must be appropriate to trial phase and particularly in mid-late-phase trials, should reflect recovery vs. compensation, as well as being clinically meaningful. The impact and extent of upper vs. lower motoneuron disease should be considered, as this may affect how an individual may respond to a given therapeutic. For trials with broad inclusion criteria, the content of COAs should cover all severities and levels of SCI. Specific measures to assess upper extremity function as well as more comprehensive COAs are under development. In addition to appropriate use of COAs, methods to increase recruitment, such as adaptive trial designs and prognostic modeling to prospectively stratify heterogeneous populations into appropriate cohorts should be considered. CONCLUSIONS: With an increasing number of clinical trials focusing on improving upper extremity function, it is essential to consider a range of factors when choosing a COA. SPONSORS: Craig H. Neilsen Foundation, Spinal Cord Outcomes Partnership Endeavor.
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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 | 0.751 | 0.849 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.013 | 0.014 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.009 | 0.007 |
| Research integrity | 0.019 | 0.017 |
| Insufficient payload (model declined to judge) | 0.008 | 0.007 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".