Leg Dominance May Not Be a Predictor of Asymmetry in Peak Joint Moments and Ground Reaction Forces During Sit-to-Stand Movements
Bibliographic record
Abstract
Sit-to-stand transfer is a common prerequisite for many daily tasks. Literature often assumes symmetric behavior across the left and right side. Although this assumption of bilateral symmetry is prominent, few studies have validated this supposition. This pilot study uniquely quantifies peak joint moments and ground reaction forces (GRFs), using a Euclidian norm approach, to evaluate bilateral symmetry and its relation to lower limb motor-dominance during sit to stand in ten healthy males. Peak joint moments and GRFs were determined using a motion capture system and inverse dynamics. This analysis included joint moment contributions from all three body planes (sagittal, coronal, and axial) as well as vertical and shearing GRFs. A paired, one-tailed t test was used, suggesting asymmetrical joint moment development in all three lower extremity joints as well as GRFs (P < .05). Furthermore, using an unpaired two-tailed t test, asymmetry developed during these movements does not appear to be predictable by participants' lower limb motor-dominance (P < .025). Consequently, when evaluating sit-to-stand it is suggested the effects of asymmetry be considered in the interpretation of data. The absence of a relationship between dominance and asymmetry prevents the suggestion that one side can be tested to infer behavior of the contralateral.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| 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) | 0.003 | 0.001 |
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".