Dietary changes and food intake in the first year after breast cancer treatment
Bibliographic record
Abstract
Understanding dietary habits of women after breast cancer is a critical first step in developing nutrition guidelines that will support weight management and optimal health in survivorship; however, limited data are available. The objective of this study was to describe changes in diet among breast cancer survivors in the first year after treatment, and to evaluate these changes in the context of current dietary intake. Changes in diet were assessed in 28 early stage breast cancer survivors, using a self-reported survey in which women identified changes in food intake since their diagnosis. Current dietary intake was estimated from 3-day food records and described relative to current recommendations. The majority of women reported changes in diet after diagnosis, most common being an increase in vegetables/fruit and fish, lower intake of red meat, and reduced alcohol. Many women reported that these changes were initiated during active treatment. Dietary changes were largely consistent with current recommendations for cancer prevention; however, some women were still above the guidelines for total and saturated fat, and many were below recommendations for vegetables/fruit, milk/alternatives, calcium, and vitamin D. Evidence that some women are willing and able to initiate positive changes in diet early in the treatment trajectory suggests that early intervention may be effective in promoting dietary habits that will assist with weight management and overall health. Data on current dietary intake highlights several possible targets for dietary intervention in this population.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".