Bibliographic record
Abstract
Compared to individuals with spinal cord injury (SCI), propulsion by older adults is poorly defined. The goal of this project is to examine the impact of wheelchair, surface, and user characteristics on propulsion mechanics in older adults and individuals with SCI. All participants self-propelled over a series of surfaces at a self-selected velocity and kinetic data collection were provided by the SmartWheel. We described a standard clinical protocol (SCP) for objective assessment of manual wheelchair propulsion and defined reference values for individuals with SCI based this protocol (N=128). The SCP requires self-propulsion over tile, low pile carpet, and up an ADA ramp. In addition we provided a decision framework based on graphical reference data; guiding clinicians through an objective assessment of propulsion, identifying opportunities for intervention and follow-up. We then compared propulsion of individuals with paraplegia (IP, N=54) and older adults (OA, N=53). OA propelled slower than IP; used a greater push frequency and minimum Mz, shorter stroke length, and similar resultant force. When surface difficulty increased, the IP group responded with increased work. This may indicate a lack of capacity in OA to respond to increased resistance. For our cohort of older adults we defined the impact of surface type, wheelchair weight, and rear axle position (N=53). As surface difficulty or chair weight increased, velocity decreased. Controlling for velocity, push frequency, resultant and tangential force increased as surface difficulty increased; heavier chairs had decreased stroke length and increased resultant and tangential force; and posterior axle positions had increased velocity. Controlling for velocity, posterior axle positions had increased forces. Finally, we examined the impact of strength and gender. Body-weight normalized grip strength was collected. Stronger individuals propel faster than weaker individuals. On low pile carpet, both genders decreased velocity versus tile, but women decreased push frequency while men increased. Surface type has a substantial impact on propulsion velocity and force; magnifying any differences between users and wheelchair configurations. Wheelchair weight and axle position independently affect propulsion mechanics. Gender and strength appear to influence propulsion. Older adults are marginal self-propellers at best; powered mobility may be a more appropriate mobility solution.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".