Exploring Robotic Devices for the Neuromuscular Population
Bibliographic record
Abstract
This 16-week capstone experience with Tampa General Hospital (TGH) focused on improving clinical practice skills in robotic-assisted technology for the adult and pediatric neuromuscular population specifically to improve functional use of the upper extremity. The capstone project is “comprised of a literature review, needs assessment, goals/objectives, and an evaluation plan based on specific focus areas” (ACOTE, 2020). Technology has provided unique opportunities for the OT treatment process. This capstone project focused on improving skills and implementation of robotic-assisted technology to increase upper extremity function. Protocols were created and explored for Tyromotion Diego, Bioness H200, and Bioness Integrated Therapy System. Implementation of this project has provided evidence of the effectiveness of the use of robotic-assisted technology, increased measurability, and a clinician guide for patient treatment. The student’s goals of this experience were to increase her skills through hands-on experience, participate in certification courses, review best practice techniques for a variety of devices, create a clinician education binder, explore measurability standards, and present her findings to TGH practitioners. The student received hands-on clinical practice using diego by tyromotion, bioness H200, BITS by bioness, and motus nova. In addition, she reviewed other evidence-based robotic devices to increase upper extremity function through a literature review. She was given a variety of learning opportunities including physical agent modalities (PAMs) certification, interactive metronome (IM) certification, Montreal Cognitive Assessment (MoCA) certification, and safe baby training. Achievement of her goals provided TGH with valuable resources including rehabilitation protocols, patient education, measurability standards, and current literature on the topic of robotic-assisted technology.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".