Moderators of the effects of exercise training in breast cancer patients receiving chemotherapy
Bibliographic record
Abstract
BACKGROUND: Exercise training improves supportive care outcomes in patients with breast cancer who are receiving adjuvant therapy, but the responses are heterogeneous. In this study, the authors examined personal and clinical factors that may predict exercise training responses. METHODS: Breast cancer patients who were initiating adjuvant chemotherapy (N=242) were assigned randomly to receive usual care (UC) (n=82), resistance exercise training (RET) (n=82), or aerobic exercise training (AET) (n=78) for the duration of chemotherapy. Endpoints were quality of life (QoL), aerobic fitness, muscular strength, lean body mass, and body fat. Moderators were patient preference for group assignment, marital status, age, disease stage, and chemotherapy regimen. RESULTS: Adjusted linear mixed-model analyses demonstrated that patient preference moderated QoL response (P= .005). Patients who preferred RET improved QoL when they were assigned to receive RET compared with UC (mean difference, 16.5; 95% confidence interval [95% CI], 4.3-28.7; P= .008) or AET (mean difference, 11; 95% CI, -1.1-23.4; P= .076). Patients who had no preference had improved QoL when they were assigned to receive AET compared with RET (mean difference, 23; 95% CI, 4.9-41; P= .014). Marital status also moderated QoL response (P= .026), age moderated aerobic fitness response (P= .029), chemotherapy regimen moderated strength gain (P= .009), and disease stage moderated both lean body mass gain (P< .001) and fat loss (P= .059). Unmarried, younger patients who were receiving nontaxane-based therapies and had more advanced disease stage experienced better outcomes. The findings were not explained by differences in adherence. CONCLUSIONS: Patient preference, demographic variables, and medical variables moderated the effects of exercise training in breast cancer patients who were receiving chemotherapy. If replicated, these results may inform clinical practice.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".