Quantifying the influence of radiation therapy on functional shoulder health indicators in breast cancer patients: An exploratory study
Bibliographic record
Abstract
INTRODUCTION: Radiotherapy is a highly effective treatment for breast cancer, but it is also associated with several complications that can impact quality of life and survivorship, including arm function. This study assessed the influence of radiation therapy on shoulder health indicators of breast cancer patients during their treatment period. METHODS: Fourteen breast cancer patients undergoing radiation therapy participated. Shoulder health indicators were assessed at the baseline, midpoint, and endpoint of radiation treatment. The indicators included shoulder muscle activations, arm circumference, shoulder complex range of motion, and arm strength. Repeated measures ANOVAs followed by post-hoc Tukey-Kramer tests were used to identify differences in shoulder indicators between levels (p < 0.05). A multiple linear regression model was created for each dependent measurement including radiation dose and fractions as predictor variables. RESULTS: The activation of the teres major and latissimus dorsi muscles decreased in most of the evaluated movements. Additionally, mean shoulder abduction decreased by 11 deg, and negative correlations existed between shoulder abduction range of motion and radiation dose, and between shoulder abduction strength and radiation fractions. The observed changes may relate to post-treatment inflammation, and lingering effects of radiation on shoulder health indicators may take longer to manifest, hindering identification within the treatment window.
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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 | 0.000 | 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".