Dietary inflammatory index and odds of colorectal cancer in a case-control study from Jordan
Bibliographic record
Abstract
Dietary components that promote inflammation of the colon have been suggested to be risk factors in the development of colorectal cancer (CRC). The possible link between inflammatory potential of diet and CRC has been investigated in several developed or Western countries. Despite the fact that dietary choices in the Middle East differ markedly from those in the West, results have not been reported from any study conducted in a Middle-Eastern population. We examined the association between dietary inflammatory index (DII) scores and CRC in a case-control study conducted in Jordan. This study included 153 histopathologically confirmed CRC cases and 202 disease-free control subjects’ frequency matched on age, sex, and occupation. Data were collected between January 2010 and December 2012, using interviewer-administered questionnaires. DII scores were computed from dietary data reported using a food frequency questionnaire. Logistic regression models were used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) adjusted for age, sex, education, physical activity, body mass index, smoking, and family history of CRC. Subjects with higher DII scores were at increased odds of CRC, with the DII being used both as a continuous variable (ORcontinuous = 1.45, 95% CI: 1.13–1.85; 1-unit increase corresponding to ≈20% of its range in the current study) and as a categorical variable (ORtertile 3 vs tertile 1 = 2.13, 95%CI: 1.23–3.72). Our results, based on a Jordanian population, add to the growing literature indicating that a pro-inflammatory diet is associated with increased odds of CRC.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".