Evaluation of a novel strategy to implement exercise evidence into clinical practice in breast cancer care: protocol for the NEXT-BRCA randomised controlled trial
Bibliographic record
Abstract
Introduction The burden of breast cancer in Canada is steadily growing. More women are surviving breast cancer, yet, survivors live with side effects for years after treatments have ended. The benefits of exercise for women with breast cancer are well established and include improvement in treatment-related physical and emotional side effects. Despite these benefits, few survivors meet exercise guidelines. Exercise programmes are needed within the cancer institution in Canada to bridge the current knowledge to practice gap. The purpose of this study is to test the effects of a novel implementation strategy that includes institution-based exercise plus self-management (SM) or SM alone versus usual care in improving exercise level, quality of life, aerobic capacity, muscle strength and use of healthcare services over 12 months for women with breast cancer receiving chemotherapy. Methods and analysis Participants: Women with stages I–III breast cancer undergoing chemotherapy. Intervention: Group 1: institution-based exercise and SM (8 exercise sessions plus 8 SM modules); Group 2: SM alone; Group 3: usual care. Outcomes: The primary effectiveness outcome is minutes per week of moderate to vigorous physical activity. Secondary outcomes include quality of life, aerobic capacity, muscle strength, and use of healthcare services. Randomisation: Participants will be randomised (1:1:1) to one of the three groups by a blinded statistician and will be stratified based on age of participant (<40, 40–60, and >60 years). Statistical analysis: Outcomes will be measured at baseline, post-intervention, 6-month and 12-month follow-up using an analysis of covariance to test changes between groups over time adjusted for age. Ethics and Dissemination This study addresses a long-standing need to help women with breast cancer undergoing chemotherapy become and stay more active by implementing novel rehabilitation strategies into real-world practice. This is vital in order for this population to minimise the lingering side effects of treatment, improve function and quality of life and prevent cancer recurrence. Trial registration number The study protocol (v1: July 2020) has been registered on ClinicalTrials.gov ( NCT04109274 ).
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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 | 0.045 | 0.048 |
| Meta-epidemiology (narrow) | 0.009 | 0.004 |
| Meta-epidemiology (broad) | 0.014 | 0.009 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.081 | 0.013 |
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".