Bibliographic record
Abstract
OBJECTIVE: The goal of this study was to determine how the Segway compares to clients' current method of mobility in meeting their specific mobility goals. DESIGN: This study included 10 subjects (aged 19-65 yrs) with a wide range of disabilities (e.g., multiple sclerosis, spinal cord injury, and amputee) who were able to walk at least 6 m with or without assistance. Subjects navigated a 25-m obstacle course at our provincial adult rehabilitation center with their current mobility devices and then the Segway. The outcome measures used were the Wheelchair Outcome Measure score and the difference in the time required to complete the obstacle course. RESULTS: There was a significant difference in Wheelchair Outcome Measure score between subjects' current mobility method and using the Segway for client specific goals (P < 0.01); however, there was no significant difference between obstacle course times. CONCLUSIONS: This study has shown that the Segway may be a good device for people with disabilities because it allows them to participate in social and functional activities in a manner that traditional mobility aids do not facilitate as well. However, it does have its limitations and should be considered as just one of the many mobility options offered to people with disabilities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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".