Upper extremity effort during the sit-to-stand task in able-bodied and in individuals with spinal cord injury: A preliminary study
Bibliographic record
Abstract
OBJECTIVE: To investigate the association between the presence of spinal cord injury (SCI) on biomechanical variables by comparing individuals with SCI and able-bodied individuals during the sit-to-stand (STS) task assisted by a walker device. Specifically, we compared the upper-extremity joint angles and moments, trunk forward tilt angle, vertical forces of the instrumented walker, and ground reaction forces between groups. DESIGN: Case-control study. SETTING: Department of Orthopedics and Traumatology, UNICAMP-Brazil. PARTICIPANTS: Six individuals with SCI and fourteen able-bodied individuals. MAIN OUTCOME MEASURES: Kinematics and kinetics of the shoulder, elbow, and wrist joints; trunk forward tilt angle, vertical walker forces, and ground reaction forces (GRF) were analyzed during the STS task in two phases: before and after the seat-off event. RESULTS: A higher peak elbow flexion angle and higher vertical walker forces were observed before the seat-off, whereas the lower peak vertical GRF was found, after the seat-off, in the SCI group compared with the control group. CONCLUSIONS: SCI affects kinematics and kinetics variables during the STS task compared to able-bodied controls. Individuals with SCI adopted different standing-up strategies that affected the distribution of the forces in the upper and lower extremities of the human body.
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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 | 0.001 | 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".