Effect of a telephone-based weight loss intervention (WLI) on weight at 12-months in women with early breast cancer: Results from the Breast Cancer Weight Loss (BWEL) trial.
Bibliographic record
Abstract
12001 Background: Obesity is a poor prognostic factor in early breast cancer. The Breast Cancer Weight Loss (BWEL) trial (Alliance for Clinical Trials in Oncology A011401; NCT02750826) evaluates the impact of a WLI on invasive disease-free survival in breast cancer patients (pts) with a body mass index (BMI) ³27 kg/m 2 . Here we report the impact of the WLI on weight change. Methods: Eligible pts were within 14 months of diagnosis of stage 2-3 HER2-negative breast cancer, had completed chemotherapy and radiation (if administered), and were randomized 1:1 to a telephone-based WLI plus health education (HE) or an HE alone control group. The WLI was delivered by telephone-based health coaching and focused on caloric restriction and increased exercise. Height and weight were measured at baseline and 12 months. Changes in weight were compared between groups. Analysis was performed with univariable and multivariable (including arm, baseline weight, menopausal status, race/ethnicity, and hormone receptor [HR] status) regression models. A 0.05 level of significance was used. Results: 3181 women were randomized between 8/2016 and 2/2021. At baseline, mean BMI was 34.5 (±5.74) kg/m 2 , mean age was 53.4 (±10.58) years, and 57% of pts were postmenopausal at the time of diagnosis. 80.3% of participants were White, 12.8% were Black, and 7.3% were Hispanic. Follow-up weight was available from 2293 pts alive and disease-free at 12 months. The WLI led to a significant decrease in weight relative to controls; pts randomized to WLI lost an average of 4.8% (±7.9) of baseline body weight at 12 months vs. 0.8% (± 6.4) weight gain in controls (p<0.0001). Pts randomized to WLI experienced significant weight loss (vs controls) across demographic and tumor factors (Table). WLI effect differed significantly by menopausal status (interaction p value = 0.0057) and race/ethnicity (interaction p-value = 0.019), but not HR status (interaction p-value = 0.17). Conclusions: A telephone-based WLI induced significant, clinically meaningful weight loss in breast cancer pts with overweight and obesity across demographic and tumor factors. Additional tailoring of the WLI could be useful to enhance weight loss in Black and younger pts. Further follow-up of the BWEL trial will evaluate whether the WLI improves disease outcomes. Support: U10CA180821, U10CA180882, UG1CA189823; https://acknowledgments.alliancefound.org . Clinical trial information: NCT02750826 . [Table: see text]
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".