Association of high adherence to vegetables-and-fruits dietary pattern with quality of life among Chinese women with early stage breast cancer
Bibliographic record
Abstract
Abstract Purpose: Dietary intake and patients’ quality of life (QoL) are important supportive care issues in breast cancer survivorship. This study aimed to identify dietary pattern after breast cancer diagnosis. In addition, the association between dietary patterns and QoL were cross-sectionally investigated.Methods: A breast cancer cohort which included 1462 Chinese women were longitudinally interviewed at four time-points, namely baseline, 18-, 36-, and 60-months after diagnosis. At each follow-up, validated food frequency questionnaires (FFQ) were used to assess patients’ dietary intake, and principal component analysis was used to derive dietary patterns. European Organization for Research and Treatment of Cancer Quality of Life Questionnaire Core 30 (EORTC QLQ-C30) were used to measure QoL. This study included 1226 patients with invasive disease who completed assessment at 18-month follow-up and had detailed data of dietary intake and QoL. Results: Two major dietary patterns were identified: “grain and animal food pattern” and “vegetables and fruits pattern”. Multivariable linear regression analysis indicated that grain and animal food intake was inversely associated with score for fatigue (P = 0.010) and dyspnea (P = 0.024). Vegetables and fruits intake were positively associated with scores for global health status/QoL (P = 0.002), while inversely associated with score for loss of appetite (P = 0.040) and diarrhea (P = 0.009). Conclusion: This study suggested that high adherence to a vegetables and fruits pattern might have beneficial effect on QoL among Chinese breast cancer patients. Prospective follow-up data could further confirm whether a specific dietary pattern has impact on cancer outcomes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".