The relationship between dietary inflammatory potential and cancer outcomes among cancer survivors: A systematic review and meta-analysis of cohort studies
Bibliographic record
Abstract
Cancer remains the second leading cause of death globally. Chronic inflammatory environments promote the growth of tumors, and the intake of certain food items can increase systemic inflammation. This study examined the relationship between the inflammatory potential of diet, measured by the Dietary Inflammatory Index (DII), and recurrence, all-cause, and cancer-specific mortality among cancer survivors. Web of Science, Medline, CINHAL, and PsycINFO databases were searched in April 2022. Two independent reviewers screened all searches. Of the 1,443 studies, 13 studies involving 14,920 cancer survivors passed all the screening stages. Three studies reported cancer recurrence, 12 reported all-cause mortality, and six reported cancer-specific mortality. Seven studies calculated DII from pre-diagnosis diets, five from post-diagnosis diets, and one from both pre-and post-diagnosis diets. A random-effects model meta-analysis showed that high DII was not associated with an increased risk of recurrence (HR = 1.09, 95 % CI = 0.77, 1.54, n = 4) and all-cause (HR = 1.08, 95 % CI = 0.99, 1.19, n = 14) and cancer-specific mortality (H = 1.07, 95 % CI = 0.92, 1.25, n = 6). Analysis by the timing of dietary assessment showed that only post-diagnosis DII was associated with an increased risk of all-cause mortality (HR = 1.34, 95 % CI = 1.05, 1.72, n = 6) by 34 %; however, cancer type did not modify these associations. The quality of the study assessed using the Newcastle Ottawa Scale indicated all but one studies were good. The risk of all-cause mortality among cancer survivors could be reduced by consuming more anti-inflammatory diets after cancer diagnosis.
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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 | 0.016 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.037 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".