Potential recovery of arm strength capability in a post-breast cancer treatment population: A simulation analysis
Bibliographic record
Abstract
Arm dysfunction often follows breast cancer treatments. Diversity in treatment makes it challenging to explore how exercise impacts dysfunction in survivors. This study computationally simulated treatment scenarios to identify a theoretical maximal producible force (aided by muscular training) and the internal muscle forces required to produce that force in a compromised system. An existing shoulder model was modified to reduce the capacity of certain muscles to mimic lower-functioning breast cancer populations. Capacity of muscles were increased to emulate training, with maximums dictated based on damage from treatment-specific scenarios (radiation, chemotherapy, combination treatment). Maximum force, torque, and muscle forces were extracted for each treatment scenario, a maximum (unaltered) non-cancer reference, and baseline (breast cancer survivor) force, across 2 maximum isometric force exertions (adduction and internal rotation). Overall, 70-80 % of strength was recoverable with successful retraining. Specifically, for both exertions' recruitment of primary movers (adductors or internal rotators) and scapular and glenohumeral stabilizers, increased from the baseline level in each scenario, with highest recruitment at the non-cancer reference force level. Although no post-training scenario reached non-cancer reference control population force levels, achieving 70-80 % of force could enable more successful daily task performance, return to work and enhance overall physical self-efficacy.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".