Development of Reaching, Grasping & Manipulation indicators to advance the quality of spinal cord injury rehabilitation: SCI-High Project
Bibliographic record
Abstract
Objective To describe the development of structure, process, and outcome indicators aimed to advance the quality of Reaching, Grasping & Manipulation (RG&M) rehabilitation for Canadians living with spinal cord injury or disease (SCI/D).Method Upper extremity rehabilitation experts developed a framework of indicators for evaluation of RG&M rehabilitation quality. A systematic search of the literature identified potential upper extremity indicators that influence RG&M outcomes. A Driver diagram summarized factors influencing upper extremity outcomes to inform the selection of structure and process indicators. Psychometric properties, clinical utility, and feasibility of potential upper extremity measures were considered when selecting outcome indicators.Results The selected structure indicator is the number of occupational and physical therapists with specialized certification, education, training and/or work experience in upper extremity therapy related to RG&M at a given SCI/D rehabilitation center. The process indicator is the total hours of upper extremity therapies related to RG&M and the proportion of this time allocated to neurorestorative therapy for each individual with tetraplegia receiving therapy. The outcome indicators are the Graded Redefined Assessment of Strength, Sensation and Prehension (GRASSP) strength and Spinal Cord Independence Measure III (SCIM III) Self-Care subscores implemented at rehabilitation admission and discharge, and SCIM III Self-Care subscore only at 18 months post-admission.Conclusion The selected indicators align with current practice, will direct the timing of routine assessments, and enhance the volume and quality of RG&M therapy delivered, with the aim to ultimately increase the proportion of individuals with tetraplegia achieving improved upper extremity function by 18 months post-rehabilitation.
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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 | 0.039 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".