Adherence to Mediterranean diet in liver transplant recipients: a cross-sectional multicenter study
Bibliographic record
Abstract
BACKGROUND: Seeing the importance of healthy diet after liver transplant (LT), our study aimed to evaluate the adherence to Mediterranean diet (MD) in a large population of LT recipients. METHODS: The present multicenter study was developed in clinically stable, liver transplanted patients, from June to September 2021. Patients completed a survey about adherence to MD, Quality of Life (QoL), sport, and employment. To analyze the correlations, we computed Pearson's coefficients; while to compare subgroups, independent samples t-tests and ANOVAs. We used a multivariable logistic regression analysis to find the predictors of impaired adherence to MD. RESULTS: The questionnaire was administered to 511 patients. They were males in 71% of cases with a mean age of 63.1 years (SD±10.8). LT recipients coming from central Italy displayed higher adherence to the MD (M=11.10±1.91) than patients from northern (M=9.94±2.28, P<0.001) or southern Italy (M=10.04±2.16, P<0.001). Patients from central Italy showed a significantly higher consumption of fruit, vegetables, legumes, cereals, olive oil, fish and a significantly lower intake of dairy products than patients resident in the other Italian areas. At multivariate analysis, recipients from central Italy were 3.8 times more likely to report adherence to the MD. Patients with a high physical health score were more adherent to MD, as well as patients transplanted at an earlier time. CONCLUSIONS: We demonstrated that place of stay, time from transplant and physical dimension of QoL significantly influences the adherence to MD. Continuous information campaigns about a correct diet and lifestyle would be necessary.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".