Upper Extremity Kinematics and Kinetics During the Performance of a Stationary Wheelie in Manual Wheelchair Users With a Spinal Cord Injury
Bibliographic record
Abstract
No comprehensive biomechanical study has documented upper extremity (U/E) kinematics and kinetics during the performance of wheelchair wheelies among manual wheelchair users (MWUs). The aim of this study was to describe movement strategies (kinematics), mechanical loads (kinetics), and power at the nondominant U/E joints during a wheelie among MWUs with spinal cord injury (SCI). During a laboratory assessment, 16 MWUs with SCI completed four wheelie trials on a rigid surface. Each participant's wheelchair was equipped with instrumented wheels to record handrim kinetics, while U/E and wheelchair kinematics were recorded with a 3D motion analysis system. The greatest mean and peak total net joint moments were generated by the shoulder flexors (mean = 7.2 ± 3.5 N·m; peak = 20.7 ± 12.9 N·m) and internal rotators (mean = 3.8 ± 2.2 N·m; peak = 11.4 ± 10.9 N·m) as well as by the elbow flexors (mean = 5.5 ± 2.5 N·m; peak = 14.1 ± 7.6 N·m) during the performance of wheelies. Shoulder flexor and internal rotator efforts predominantly generate the effort needed to lift the front wheels of the wheelchair, whereas the elbow flexor muscles control these shoulder efforts to reach a state of balance. In combination with a task-specific training program that remains essential to properly learn how to control wheelies among MWUs with SCI, rehabilitation professionals should also propose a shoulder flexor, internal rotator, and elbow flexor strengthening program.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".