Nutrient patterns and risk of esophageal squamous cell carcinoma: a case-control study
Bibliographic record
Abstract
Although Iran is a high-risk country for esophageal squamous cell carcinoma (ESCC), the contribution of overall nutrient intakes to this high incidence rate is not yet clear. The aim of this study was to examine the association between nutrient patterns and risk of ESCC in Iran. Forty-seven patients with ESCC and 96 frequency-matched hospital controls underwent private interviews, and dietary habits were collected using a validated food-frequency questionnaire. Factor analysis was conducted and two major nutrient patterns were retained; factor 1 (high in pantothenic acid, vitamin C, potassium, vitamin B(6), magnesium, folate, thiamin, copper, carbohydrate, vitamin K, niacin, α-tocopherol, zinc, total fiber, fluoride, and polyunsaturated fatty acids) and factor 2 (high in saturated fatty acid, biotin, selenium, monounsaturated fatty acids, riboflavin, sodium, fat, cholesterol, calcium, phosphorus, protein, iron, vitamin E, manganese, vitamin D, and vitamin B(12)). Factor 2 was inversely associated with ESCC (OR = 0.06, 95% CI: 0.01-0.28; P = 0.008), whereas no significant association was found for factor 1 (OR = 0.45, 95% CI: 0.11-1.82). The results of the present study suggested a possible role for a nutrient pattern similar to factor 2 in reducing the risk of ESCC.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".