Weight changes of younger and older early breast cancer patients—a meta regression
Bibliographic record
Abstract
BACKGROUND: Weight gain during chemotherapy for breast cancer is quite common and has a major impact on the quality of life. Post-treatment weight gain can also impact on primary endpoints such as tumor recurrence and overall survival. Parameters thought to impact weight gain include menopausal status, age and chemotherapy regimen. Using meta-regression, we studied the effect of age on weight change by menopausal status and chemotherapy regimen. METHODS: Twenty-four studies were identified, and extracted for weight change, mean/median age, menopausal status and chemotherapy regimen. A meta-regression was performed using a random-effects model for high heterogeneity and fixed-effects inverse-variance model for low heterogeneity. Subgroup analyses by menopausal status and chemotherapy regimen were conducted. P values <0.05 were considered statistically significant. RESULTS: There exists no relationship between weight change and age (β=0.00; P=0.987). Stratifying by menopausal status (β=0.05 and P=0.150 for premenopausal patients; β=0.09 and P=0.588 for postmenopausal patients) and chemotherapy regimens (β=-0.07 and P=0.562 for patients receiving CMF alone; β=0.08 and P=0.707 for patients receiving CMF in addition to others; β=0.02 and P=0.807 for patients not receiving CMF), there likewise was no relationship between weight change and age. CONCLUSIONS: Management of weight gain due to chemotherapy has been focused on relatively young women who are generally at higher risk of mortality and tumor recurrence. However, our results suggest that age should not be used for differential care.
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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 | 0.018 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.073 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".