Tailoring the Evidence for Exercise Oncology within Breast Cancer Care
Bibliographic record
Abstract
Exercise is safe and effective for those living with and beyond breast cancer, with evidence supporting exercise guidelines, and position statements from international organizations. Despite the clearly recognized benefits of exercise for these individuals, many do not participate or maintain recommended exercise levels throughout the breast cancer continuum, highlighting the lack of translation from research into practice. In addition, discerning how exercise can be tailored to address breast cancer-related impairments, so that individuals are able to participate safely and effectively, has also not been studied extensively. Thus, we propose that implementing exercise screening, triage, and referral pathways across the breast cancer continuum may allow for increased accessibility and adoption among those living with and beyond breast cancer. This paper provides an overview of exercise prescription tailoring for common breast cancer and treatment-related impairments, proposes a simplified screening tool for identifying physical activity and movement-related impairments, and considers how best to channel evidence into practice via proposed implementation pathways that may better connect individuals living with and beyond breast cancer with exercise oncology resources through screening, triage, and referral.
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".