Weight Change of Younger and Older Early Breast Cancer Patients – A Meta Regression
Bibliographic record
Abstract
Abstract Introduction: Weight gain has a major impact on the quality of life of breast cancer patients. Post treatment weight gain can impact on primary endpoints such as recurrence, death, self identity and the ability to return to work. Parameters thought to impact on 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: 24 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 DerSimonian and Laird model for high heterogeneity and fixed-effects inverse-variance model for low heterogeneity. Subgroup analyses were conducted, by menopausal status and chemotherapy regimen. P-values less than 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 and chemotherapy regimens, there likewise was no relationship. Conclusion: Management of weight gain due to chemotherapy has been focused on relatively young women where a higher mortality and recurrence has been found. However, our results suggest that age should not be used to differentiate care in these patients.
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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 | 0.022 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.063 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".