The relationship between dietary inflammatory potential and cancer outcomes among cancer survivors: A systematic review and meta-analysis of cohort studies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.008 | 0.001 |
| 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) | 0.000 | 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 it