A parametric 3D printed assistive device for people with cerebral palsy – assessment of outcomes and comparison with a commercial counterpart
Bibliographic record
Abstract
This paper presents the use of a parametric design platform and 3D printing to create personalized assistive devices (ADs) for individuals with cerebral palsy, specifically a fork/spoon holder.Five subjects participated in the study, each receiving a customized device to fit their dominant hand, and they tested both the 3D printed device and a commercially available one.The devices were evaluated based on functionality and satisfaction using two standard clinical questionnaires (IPPA and QUEST 2.0).The results showed that neither the 3D printed nor the commercial device provided substantial benefits, but the questionnaires provided valuable feedback on areas for improvement.The study highlights the need for highly personalized solutions in ADs, which could be addressed by 3D printing.A configurator for generating production files from a parametric model could facilitate personalization, but a large number of model versions should be available to meet individual needs.Future research could explore clinical evaluations and guide the development of efficient and effective frameworks for digital fabrication in terms of clinically feasible AD model sourcing. Implication for rehabilitation* 3D printing can be a method of creating assistive devices with dimensions fitting the user.* Standard questionnaires for measuring the effectiveness and satisfaction may efficiently identify shortcomings and suggest improvements.* Each person may have individual requirements, which calls for a large database of solutions in order to meet the person's needs.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.001 | 0.000 |
| 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".