Validation of an FFQ for evaluation of EPA and DHA intake
Bibliographic record
Abstract
OBJECTIVE: To validate an FFQ for the assessment of dietary EPA and DHA against their relative concentrations in red blood cells (RBC). DESIGN: Cross-sectional analysis of baseline data. Intakes of marine food products and EPA and DHA were estimated by FFQ on the basis of consumption of marine food products in the last month. Fatty acid composition of RBC membranes was quantified by GC. SETTING: Saint-François d'Assise Hospital, Québec, Canada. SUBJECTS: A total of sixty-five middle-aged women who participated in a randomized clinical trial. RESULTS: Spearman's correlation coefficient between intake of EPA, DHA and EPA + DHA and their corresponding concentration in RBC was 0.46, 0.40 and 0.42, respectively (all P < 0.05). Multiple regression analysis of EPA+DHA intake and RBC EPA + DHA concentration indicated positive and significant correlations for oily fish (beta = 0.44, 95% CI 0.16, 0.72, P = 0.0027), total fish (beta = 0.42, 95% CI 0.19, 0.64, P = 0.0005) and marine food products (beta = 0.42, 95% CI 0.20, 0.64, P = 0.0003). No other marine food products significantly predicted RBC EPA + DHA concentration. CONCLUSIONS: Although the present validation study was undertaken among middle-aged women with low consumption of marine food products (<3 servings/week), our FFQ provided estimates of EPA and DHA intakes that correlated fairly well with their RBC concentrations. However, the absence of correlations between EPA + DHA intakes from different marine species suggests that a minimum EPA + DHA intake is necessary to observe a relationship with RBC EPA + DHA concentrations.
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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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".