Chemotherapy-Induced Changes in Body Composition among Breast Cancer Patients: A Scoping Review
Bibliographic record
Abstract
The fundamental use of chemotherapy in breast cancer treatment leads to substantial body composition alterations which cause sarcopenia combined with fat deposition as well as body weight irregularities. Following chemotherapy, there are changes that impact the patient's tolerance as well as their overall health and prognosis. Such changes in body composition require precise understanding for the improvement of patient care and effective treatment development. This study used following databases, including PubMed, OVID and PEDro and the eligibility and screening process led to the selection of nine studies in the review. Nine studies examined weight and body composition changes during chemotherapy in breast cancer patients, revealed significant increases in body weight and fat mass. Notably, gaining weight while undergoing chemotherapy was linked to worse survival outcomes, such as a decline in overall survival (OS) and disease-free survival (DFS). Furthermore, sarcopenia has a detrimental effect on the results of chemotherapy, increasing toxicity and decreasing response to treatment. Comparative studies indicated that breast cancer patients experienced increase fat mass and decreased lean body mass during chemotherapy compared to controls without cancer, with these changes persisting after treatment. Patients receiving chemotherapy developed changes in their body composition that result in sarcopenia and fat mass gain. The alterations cause lower chemotherapy effectiveness and elevated chemotherapy toxicity levels. Understanding these changes is essential for patient assessment, therapeutic development and complication prevention.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".