An evaluation of the feasibility and clinical utility of the Diego™ computer-assisted robotics device for use with people with a cervical spinal cord injury in the acute setting: a mixed method pilot study
Bibliographic record
Abstract
Purpose This study aimed to determine the feasibility and clinical utility of the Diego™ for people with a cervical spinal cord injury (CSI) in an acute spinal ward.Materials and methods A mixed methods study included prospective measurement of outcomes and qualitative interviews with participants and health professionals. A 22 day (4.4 weeks Monday to Friday) program incorporating baseline and follow up measurements, 2 × 1-h standard upper limb sessions per week, and 3 × 1-h Diego™ sessions per week were designed for implementation. Participants were assessed on recruitment and at the completion of the program.Results Seven eligible patients (6 male and 1 female) and eight health professionals participated. Participants improved in most muscle strength and range of motion scores across upper limb joints although these were not statistically significant, Spinal Cord Independence Measure scores (14.29 at baseline to 18.29 at follow up, p = 0.01) and fatigue scores (reduction of 1.57 points, p = 0.577). Pain scores remained stable. The Canadian Occupational Performance Measure indicated improvements in performance (+2.20 points, p = 0.028) and satisfaction scores (+2.53 points, p = 0.028). Qualitative findings from both participants and health professionals indicated that participants experienced psychological benefits using the Diego™.Conclusions Some gains in functioning occurred. Further research should include a randomised controlled trial to fully evaluate the effectiveness of the Diego™ in acute settings.
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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 | 0.026 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".