Integrating 3D-Printed Task-Specific Terminal Devices With a Standard Myoelectric Prosthesis in a Patient With Systemic Scleroderma and Transradial Amputation: A Case Report
Bibliographic record
Abstract
Objective: The aim of the study was to evaluate the effectiveness of personalized task-specific 3D-printed terminal devices integrated with a standard myoelectric prosthesis in improving functional independence and comfort in a patient with systemic scleroderma and transradial amputation. Methods: A 57-year-old female patient with systemic scleroderma and a left transradial amputation used the following three task-specific 3D-printed adaptive terminal devices—a sock aid, buttoning tool, and jar opener—developed using Tinkercad and fabricated with PLA via FDM printing. These devices were integrated into her pre-existing standard myoelectric prosthesis (Ottobock MyoFacil, four-channel transradial model), which the patient had already been using for daily activities. Functional outcomes were assessed using the Canadian Occupational Performance Measure, Functional Independence Measure (FIM), and visual analog scale (VAS) for pain. Results: Canadian Occupational Performance Measure scores increased from 2/10 to 7–8/10 in performance and satisfaction. Functional Independence Measure scores improved from 4 to 6, reflecting reduced need for assistance in self-care. VAS scores decreased from 7/10 to 3/10, indicating reduced pain during activities of daily living. Conclusions: The integration of low-cost, patient-specific 3D-printed terminal devices with an existing myoelectric prosthesis significantly improved function, independence, and comfort. This case supports further exploration of additive manufacturing as a complementary strategy to enhance prosthetic function in individuals with rare and complex impairments. Clinical Relevance: 3D printing offers a scalable, adaptable solution for task-specific 3D-printed terminal devices, particularly in patients with systemic comorbidities.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".