Using a Patient-Completed Food Frequency Questionnaire to Determine Mediterranean Diet Score in People with Inflammatory Bowel Disease
Bibliographic record
Abstract
Purpose: To examine the level of agreement between a patient-completed food frequency questionnaire (FFQ) and assessment of usual intake by a registered dietitian (RD) to score adherence to a Mediterranean diet (MedD) in patients with inflammatory bowel disease (IBD). Methods: Patients with IBD completed a short FFQ and were subsequently interviewed by an RD. A 12-item MedD score (MDS), adapted from the Mediterranean Diet Adherence Screener (MEDAS), was calculated from the FFQ and RD assessments. To determine agreement between individual items, Cohen’s kappa coefficients were calculated. Absolute agreement between assessment methods was quantified using a one-way random intra-class correlation coefficient for a single measure. Results: Forty-six patients with IBD participated. The mean FFQ-MDS was 4.59 (standard deviation [SD] = 1.65), and mean RD-MDS was 4.83 (SD = 1.53). Kappa coefficients for individual MEDAS items ranged from 0.41 to 0.78 (p < 0.01) between the FFQ- and RD-MDS. Most items demonstrated moderate to substantial agreement. The intra-class correlation coefficient for absolute agreement between the summed FFQ-MDS and RD-MDS was 0.71 (95% confidence interval: 0.52–0.83, p < 0.001), indicating moderate reliability. Conclusions: This patient-completed FFQ may be a promising tool in clinical practice and research and would benefit from additional evaluation to validate its use in patients with IBD.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".