Diet and Risk of Cholecystectomy: A Prospective Study Based on the French E3N Cohort
Bibliographic record
Abstract
OBJECTIVES: This study aimed to examine the relationship between diet and cholecystectomy risk, using three approaches, in a large French cohort. METHODS: In a prospective cohort study in French women who completed a food-frequency questionnaire at baseline, we analyzed diet with three approaches: food groups, dietary patterns obtained by factor analysis, and the Mediterranean diet score. The primary outcome was cholecystectomy. We used Cox proportional hazards regression to assess the relationship between diet and cholecystectomy risk, adjusting for the main potential confounders. RESULTS: During 1,033,955 person years of follow-up, we identified 2,778 incident cases of cholecystectomy. Higher intakes of legumes, fruit, vegetable oil, and wholemeal bread were associated with decreased cholecystectomy risk. Two dietary patterns were identified by factor analysis: "Western" (essentially processed meat, pizza, pies, high-alcohol beverages, French fries, sandwiches…) and "Mediterranean" (essentially fruits, vegetables, seafood, and olive oil). The "Mediterranean" pattern was inversely associated with cholecystectomy risk in the subgroup of postmenopausal women who ever used menopausal hormone therapy (hazard ratio for quartile 4 vs. 1=0.79, 95% confidence interval (CI): 0.65-0.95; P for linear trend=0.008). High adherence to the Mediterranean diet was associated with decreased risk of cholecystectomy (hazard ratio for a 6-9 score vs. 0-3=0.89, 95% CI: 0.80-0.99; P for linear trend=0.02). CONCLUSIONS: Adherence to a diet rich in fruit, vegetables, legumes, and olive oil was associated with a reduction in cholecystectomy risk in French women. Further studies in different settings are requested.
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.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.001 |
| 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".