On the road again! Tricycle adaptation with the design of a universal rig
Bibliographic record
Abstract
Cerebral palsy is the most common childhood disability impacting motor function. The International Classification of Functioning, Disability and Health defines outcomes that should be achieveable within the Activities and Participation domain. However, many children with cerebral palsy have significant difficulties in achieving activity goals within a typical recreational environment. Despite the well documented benefits of cycling for persons with cerebral palsy for example, it is often difficult to access commercially available adaptive tricycles due to prohibitive costs and varying needs. Even commercially available adaptive tricycles sometimes need to be customized. This paper outlines the design and implementation of a custom tricycle adaptation for a teenager with cerebral palsy, who was previously unable to complete a pedal rotation on any of the many adaptive tricycles she tried. The first phase of the project was the design and implementation of a "test rig" system that allowed different tricycle adaptations to be tested with the client, and could be used with any client. The second stage included two iterations of the design and implementation of adaptations to the tricycle. The final modifications enabled the client to ride independently. Challenges, successes, and recommendations for helping similar clients gain access to cycling are highlighted.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".