The Breast Cancer Weight Loss trial (Alliance A011401): A description and evidence for the lifestyle intervention
Bibliographic record
Abstract
Abstract The Breast Cancer Weight Loss (BWEL) trial is a randomized controlled trial designed to determine whether weight loss after a breast cancer diagnosis can reduce the risk of cancer recurrence in women with overweight or obesity. The BWEL trial will compare the efficacy of a telephone‐based weight‐loss intervention plus health education materials versus health education materials alone on invasive disease‐free survival in 3,181 women with stage II or III breast cancer and BMI > 27 kg/m2. This report provides a detailed description of the goals and methods of the lifestyle intervention and the evidence supporting the intervention used in the BWEL trial. The intervention’s primary goal for participants is to achieve and maintain a weight loss ≥ 10% of baseline weight through increased physical activity and caloric restriction. The evidence supporting the diet, physical activity, and behavioral components of this telephone‐based weight‐loss intervention, as well as strategies to promote participant engagement and retention, is described. The intervention is provided through 42 sessions delivered by trained health coaches over a 2‐year period. If the BWEL lifestyle intervention is successful in improving cancer outcomes, then weight loss will be incorporated into the care of thousands of breast cancer patients.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".