A case-control study of pectoralis major muscle quality in breast cancer survivors after radiation therapy without prior axillary lymph node dissection
Bibliographic record
Abstract
Radiation therapy for breast cancer is associated with considerable radiation to the pectoralis major, which causes muscle fibrosis and reduces muscle quality. The present study examined pectoralis major muscle quality after radiation therapy in breast cancer survivors using ultrasound echo intensity (EI). Twelve breast cancer survivors treated with radiation therapy to breast and axilla (breast + axilla group) and 12 survivors treated with radiation therapy to the breast only (breast only group) were compared to 12 cancer-free control women. All breast cancer survivors underwent breast-conserving surgery and sentinel node biopsy before radiation therapy. The pectoralis major was imaged with ultrasound on the treated side in breast cancer survivors and a randomly chosen side in controls. EI values were acquired from these images as a measure of muscle quality. Total body fat percentage was obtained from dual X-ray absorptiometry scans. EI of the pectoralis major differed between the groups ( p = 0.013), where EI values were higher in the breast + axilla group ( p = 0.049) and breast-only group ( p = 0.013) compared to controls. Mediation-based regression analysis showed that the effect of radiation on EI is significantly mediated by body fat percentage. Higher pectoralis major EI in the breast cancer groups suggests lower muscle quality after radiation therapy, which may contribute to the shoulder functional deficits experienced by many breast cancer survivors. The findings suggest that this poorer muscle quality may result from higher body fat percentage and is exacerbated by radiation exposure.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".