Calibration of the dietary questionnaire for the Canadian Study of Diet, Lifestyle and Health cohort
Bibliographic record
Abstract
OBJECTIVE: For proper interpretation of results from epidemiological studies that use food-frequency questionnaires (FFQs), it is necessary to know the relationship between reported intakes from the FFQ and true usual intake. In this paper, we report a calibration study conducted to investigate the performance of the FFQ used in a cohort study, the Canadian Study of Diet, Lifestyle and Health. METHODS: Over a 1-year period, 151 men and 159 women completed a full set of questionnaires including a self-administered baseline FFQ, three 24-hour diet recalls administered by telephone, and a second FFQ self-administered subsequently. The association between the nutrient estimates derived from the FFQs and the diet recalls was evaluated by calculating deattenuated Pearson's correlation coefficients. RESULTS: FFQs estimated mean daily nutrient intakes higher than the diet recalls. When the log-transformed and energy-adjusted nutrient intakes from the average of three 24-hour recalls were compared against the baseline FFQ, the following deattenuated correlations were obtained in men and women, respectively: total energy 0.44 and 0.32, total fat 0.64 and 0.68, saturated fat 0.68 and 0.70, dietary fibre 0.65 and 0.44, vitamin E 0.32 and 0.37, vitamin C 0.40 and 0.37, beta-carotene 0.34 and 0.29, alcohol 0.74 and 0.67, caffeine 0.81 and 0.76, with a median correlation of 0.49 and 0.53. Correlations between the second FFQ and diet recalls were similar. The correlations between the two FFQs as a test of reliability had a median value 0.64 for men and 0.63 for women for selected nutrients. CONCLUSIONS: The study suggests that the FFQ method gives acceptable levels of nutrients or food component estimates, as assessed by this calibration study against diet recalls, when limited to energy-adjusted and deattenuated values.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".