Development and validation of a food-frequency questionnaire for the determination of detailed fatty acid intakes
Bibliographic record
Abstract
Abstract Objective To validate a fat intake questionnaire (FIQ) developed to assess habitual dietary intake while focusing on the assessment of detailed fatty acid intake including total trans unsaturated fatty acids (TUFA). Design An 88 food item/food group FIQ was developed using a meal pattern technique. Validation was achieved by comparison with dietary intake assessed by a modified diet history (DH) in a cross-over design. Eighty-four individuals supplied adipose tissue biopsies for linoleic acid and total TUFA analysis as an independent validation of the FIQ and DH. Setting Medical Centre, Dublin Airport, Republic of Ireland. Subjects One hundred and five healthy volunteers (43 females and 62 males aged 23–63 years). Results Significant correlations (P < 0.0005) were achieved for intakes of energy (0.78), total fat (0.77), saturated fat (0.77), monounsaturated fat (0.63), polyunsaturated fat (0.73), TUFA (0.67) and linoleic acid (0.71) assessed by the FIQ compared with the DH. Linoleic acid intake assessed by the FIQ and the DH was significantly correlated with adipose tissue concentrations (r = 0.58 and 0.49, respectively; P<0.005); however, total TUFA intake was poorly correlated with adipose tissue concentrations (r = 0.17 and 0.10 for FIQ and DH, respectively). Conclusions The FIQ compared favourably with the DH in assessing habitual diet, in particular fatty acid intake. In addition, the FIQ was successfully validated against the linoleic acid composition of adipose tissue, an independent biomarker of relative fatty acid status. The FIQ could therefore be used as an alternative to the DH as it is a shorter, less labour-intensive method.
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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 | 0.018 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".