Challenging Horizontal Movement of the Body During Sit-to-Stand: Impact on Stability in the Young and Elderly
Bibliographic record
Abstract
There are 3 significant challenges to sit-to-stand: (a) bringing the center of mass forward, (b) vertically raising the center of mass from the sitting to standing position, and (c) transition from a relatively large and stable base of support in sitting to a considerably smaller base of support when standing. The authors explored the challenges to stability control following sit-to-stand when the requirement for horizontal movement of the center of mass was influenced by foot position and their potential effect on the preceding phases of sit-to-stand. Eleven healthy young and 11 healthy elderly individuals performed the sit-to-stand with their feet further away and closer to the chair. Kinetic and kinematic data were recorded. Regardless of foot position, challenges in stability were greater in elderly participants than young participants despite their similar movement time and shear forces. The greater instability in elderly participants, despite their comparable movement characteristics, emphasizes the importance of stability control following sit-to-stand performance. For both young and elderly participants, the sit-to-stand duration and the shear forces were greater in the far condition. However, foot position did not affect the stability measures (i.e., duration of the stabilization phase and the total center of pressure path during the 1st second of the stabilization phase).
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".