Diet Quality among Cancer Survivors and Participants without Cancer: A Population-Based, Cross-Sectional Study in the Atlantic Partnership for Tomorrow’s Health Project
Bibliographic record
Abstract
Cancer survivors are encouraged to have a healthy lifestyle to reduce health risks and improve survival. An understanding of health behaviors, such as diet, is also important for informing post-diagnosis support. We investigated the diet quality of cancer survivors relative to participants without cancer, overall and by cancer site and time from diagnosis. A cross-sectional study design within the Atlantic PATH study was used which included 19,973 participants aged 35 to 69 years from Atlantic Canada, of whom 1,930 were cancer survivors. A diet quality score was derived from a food frequency questionnaire. Comparisons of diet quality between cancer survivors and non-cancer controls, cancer site and years since diagnosis were examined in multivariable multi-level models. Cancer survivors had a mean diet quality of 39.1 out of 60 (SD: 8.82) and a higher diet quality than participants without cancer (mean difference: 0.45, 95% CI: 0.07, 0.84) after adjustment for confounders. Odds of high diet quality was greater in breast cancer survivors than participants without cancer (OR = 1.42, 95% CI: 1.06, 1.90), and higher among survivors diagnosed ≤2 years versus >10 years (OR = 1.71, 95% CI: 1.05, 2.80). No other differences by cancer site and years since diagnosis were observed. The difference in diet quality, although statistically significant, is unlikely to be meaningful, suggesting that cancer survivors have similar diet quality as participants without cancer. There was considerable room for dietary improvement regardless of cancer status, highlighting the need for dietary interventions, especially among cancer survivors, who are at higher risk for secondary health problems.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".