Validation of nutrient intake using an FFQ and repeated 24 h recalls in black and white subjects of the Adventist Health Study-2 (AHS-2)
Bibliographic record
Abstract
OBJECTIVE: To validate a 204-item quantitative FFQ for measurement of nutrient intake in the Adventist Health Study-2 (AHS-2). DESIGN: Calibration study participants were randomly selected from the AHS-2 cohort by church, and then subject-within-church. Each participant provided two sets of three weighted 24 h dietary recalls and a 204-item FFQ. Race-specific correlation coefficients (r), corrected for attenuation from within-person variation in the recalls, were calculated for selected energy-adjusted macro- and micronutrients. SETTING: Adult members of the AHS-2 cohort geographically spread throughout the USA and Canada. SUBJECTS: Calibration study participants included 461 blacks of American and Caribbean origin and 550 whites. RESULTS: Calibration study subjects represented the total cohort very well with respect to demographic variables. Approximately 33 % were males. Whites were older, had higher education and lower BMI compared with blacks. Across fifty-one variables, average deattenuated energy-adjusted validity correlations were 0.60 in whites and 0.52 in blacks. Individual components of protein had validity ranging from 0.40 to 0.68 in blacks and from 0.63 to 0.85 in whites; for total fat and fatty acids, validity ranged from 0.43 to 0.75 in blacks and from 0.46 to 0.77 in whites. Of the eighteen micronutrients assessed, sixteen in blacks and sixteen in whites had deattenuated energy-adjusted correlations >or=0.4, averaging 0.60 and 0.53 in whites and blacks, respectively. CONCLUSIONS: With few exceptions validity coefficients were moderate to high for macronutrients, fatty acids, vitamins, minerals and fibre. We expect to successfully use these data for measurement error correction in analyses of diet and disease risk.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".