Changes in Weight and Body Composition Among Women With Breast Cancer During and After Adjuvant Treatment
Bibliographic record
Abstract
BACKGROUND: Antineoplastic adjuvant treatment for breast cancer can cause changes in women's weight and body composition and influence their general health and survival. OBJECTIVE: The aim of this study is to investigate the extent and patterns of change in weight and body composition after current standard adjuvant antineoplastic treatment for breast cancer. METHODS: Data on weight and body composition from 95 women with breast cancer Stage I to III were obtained during 18 months on a bioelectric impedance analyzer. Changes and odds ratio (OR) were calculated by a linear mixed model and logistic regression. RESULTS: At 18 months, there was an increase in weight of 0.9 kg (95% confidence interval [CI], 0.3-1.5; P = .003) and an average positive association of 0.35 kg/cm increased waist circumference (95% CI, 0.29-0.42 kg; P < .0001). Relative weight changes ranged from -12.7% to 20.5%. Weight gains related to increased body fat were observed mainly in premenopausal women receiving chemotherapy (1.4 kg; 95% CI, 0.4-2.4; P = .007). For menopausal status, OR was 2.9 (95% CI, 1.14-7.1; P = .025), and for chemotherapy, OR was 2.6 (95% CI, 1.03-6.41; P = .043). The OR for weight loss in Stage III breast cancer was 12.5 (95% CI, 1.21-128.84; P = .034) and 4.3 (CI, 1.07-17.24; P = .40) for comorbidity. CONCLUSIONS: Results demonstrate that weight changes in a pooled sample are overestimated. However, premenopausal women receiving anthracycline-based chemotherapy show a tendency toward a body composition with increasing fat mass. IMPLICATIONS FOR PRACTICE: A scheduled assessment of changes in weight and body composition is relevant at 18 months after treatment. To compare future studies, common measuring and cutoff points are needed.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".