Young and older adult females show increased muscle compensation compared to males while moving an external load for three functional tasks
Bibliographic record
Abstract
Rotator cuff tears (RCT) are common. Prior work studied effects of RCT severity, load, and task on functional performance, but no work has examined the combined effect of factors or age- and sex-based effects, which was the objective of this study. Models representing young/older adult males/females with varying RCT severity were used. Seven external loads were applied to the hand during 3 functional tasks. Multiple 2-way ANOVAs and exploratory mixed effects model were used to determine muscle force differences between RCT severity, external load, sex, age, and task. Root mean squared error (RMSE) was used to quantify hand deviation. The deltoid, pectoralis major, infraspinatus, and teres minor muscles increased force for interactions of RCT severity, external load, age, sex, and task and the following main effects: RCT severity (all p < 0.0043), external load (all p < 0.0001), female sex (all p < 0.0001), age (all p < 0.0214), and multi-planar tasks (all p < 0.0001). Larger force contributions occurred for female/older adults with increased RCT severity/external load. Greatest hand deviation occurred for massive tear severity, 66.7 N load for all model permutations (all RMSE > 0.0077 m), with greater deviation for multi-planar vs planar tasks. Study outcomes aid in development of sex, age, and RCT severity specific rehabilitation that incorporates multi-planar movement.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.006 | 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".