Comparison of peak shoulder and elbow mechanical loads during weight-relief lifts and sitting pivot transfers among manual wheelchair users with spinal cord injury
Bibliographic record
Abstract
This study compared shoulder and elbow joint forces and moments between weight-relief lifts (WRLs) and sitting pivot transfers (SPTs) among manual wheelchair users with spinal cord injury (SCI) (N = 13) during biomechanical laboratory assessment. Minimum and maximum values were reported for each triaxial component of the joint force at the dominant shoulder and elbow during SPTs (leading and trailing roles) and WRLs. Peak shoulder flexor and adductor moments, along with elbow flexor and extensor moments, observed during the same period were also analyzed. The SPTs predominantly exposed (p < 0.001) the shoulder joints to substantial posteriorly directed forces (leading = -2.6 N/kg; trailing = -3.1 N/kg) compared with WRLs (-2.2 N/kg), whereas superiorly directed forces (2.9 N/kg) were principally sustained ( p < 0.001) during WRLs compared with SPTs (leading = 1.5 N/kg; trailing = 1.5 N/kg). High superiorly directed forces (3.6 to 3.9 N/kg) were observed at the elbow, which were comparable (p = 0.33) between the two tasks. The peak shoulder flexor (leading = 1.36 N m/kg; trailing = 1.45 N m/kg) and adductor moments (leading only = -0.46 N m/kg), along with the peak elbow flexor moments (leading = 0.24 N m/kg; trailing = 0.15 N m/kg), were significantly more elevated (p < 0.021) during SPTs than during WRLs. Peak shoulder adductor (-0.46 vs -0.24 N m/kg) and elbow flexor moments were also more elevated ( p = 0.03) at the leading upper limb compared with the trailing one. The peak elbow extensor moments did not differ ( p = 0.167) between the two tasks (-0.17 to -0.25 N m/kg). SPTs exposed the shoulder and elbow joints to greater mechanical loads than WRLs among individuals with SCI.
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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".