Estimating Muscle Forces for Breast Cancer Survivors During Functional Tasks
Bibliographic record
Abstract
Breast cancer survivors have known scapular kinematic alterations that may be related to the development of secondary morbidities. A measure of muscle activation would help understand the mechanisms behind potential harmful kinematics. The purpose of this study was to define muscle force strategies in breast cancer survivors. Shoulder muscle forces during 6 functional tasks were predicted for 25 breast cancer survivors (divided by impingement pain) and 25 controls using a modified Shoulder Loading Analysis Module. Maximum forces for each muscle were calculated, and 1-way analysis of variance (P < .05) was used to identify group differences. The differences between maximum predicted forces and maximum electromyography were compared with repeated-measures analysis of variance (P < .05) to evaluate the success of the model predictions. Average differences between force predictions and electromyography ranged from 7.3% to 31.6% but were within the range of previously accepted differences. Impingement related pain in breast cancer survivors is associated with increased force of select shoulder muscles. Both pectoralis major heads, upper trapezius, and supraspinatus peak forces were higher in the pain group across all tasks. These force prediction differences are also associated with potentially harmful kinematic strategies, providing a direction for possible rehabilitation strategies.
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 | 0.000 | 0.002 |
| 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) | 0.001 | 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".