Quantification of reaction forces during sitting pivot transfers performed by individuals with spinal cord injury
Bibliographic record
Abstract
OBJECTIVES: To quantify the reaction forces exerted under the hands, feet and buttocks when individuals with spinal cord injury performed sitting pivot transfers. DESIGN: Twelve men with paraplegia completed 3 transfers between seats of the same height (0.5 m high) and 3 transfers to a high target seat (0.6 m high). RESULTS: Greater mean and peak vertical reaction forces were always recorded under the hands compared with the feet (p<0.001) during the transfers. Mean vertical reaction forces were similar between the leading and trailing hands (p>0.088) for the 2 transfers studied. However, the mean vertical reaction force underneath the leading hand was greater when transferring between a seat of the same height compared with one of a higher height (p=0.021) and vice-versa for the trailing hand (p=0.0001). The peak vertical reaction force always occurred earlier (p<0.0001) and was greater underneath the trailing hand compared with the leading one (p<0.02), and reached its highest value when transferring to the high target seat (p=0.003). Peak and mean horizontal reaction forces were always higher underneath the trailing hand compared with the leading hand (p<0.001). CONCLUSION: These results provide evidence-based data to better understand transfers and strengthen clinical practice guidelines targeting the preservation of upper extremity integrity.
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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) | 0.001 | 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".