Patterns of Weight and Body Composition Change in Premenopausal Women With Early Stage Breast Cancer
Bibliographic record
Abstract
The widely documented problem of weight gain during adjuvant breast cancer chemotherapy has decreased in frequency and magnitude. However, adverse changes in body composition remain a problem. This study identified the frequency, magnitude, and patterns of weight and body composition change in a sample of premenopausal breast cancer survivors who were receiving 3 common chemotherapy regimens. The longitudinal study followed 76 women at 2 centers in Ontario, Canada. Measures were obtained at baseline, the start of every other treatment cycle and treatment completion. Participants' mean age was 44.1 years (SD = 5.9). Their mean baseline weight and body mass index were 69.3 kg (SD = 17.0) and 26 kg/m2 (SD = 6.6), respectively. Fifty-five percent maintained stable weights, while 34% gained and 10.5% lost weight. Their mean weight change during treatment was a 1.4-kg gain. Weight gainers and losers gained or lost 3 to 4 times as much fat as fat-free mass, respectively. A researcher's definition of "weight change" will influence the amount of weight gain reported, and the results of this study suggest that previous research may have overestimated the frequency and magnitude of weight gain in this population. Further research is needed to design interventions that match survivors' needs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| 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) | 0.000 | 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".