Validation of a semiquantitative food‐frequency questionnaire measuring dietary vitamin K intake in elderly
Bibliographic record
Abstract
The study objective was to validate a semiquantitative food frequency questionnaire (FFQ) designed to measure dietary vitamin K (VK) intake specifically. A 50‐item FFQ was interviewer‐administered and compared with data previously obtained from 5‐d food records (FR). Thirty‐nine community‐dwelling healthy men and women aged 65 to 85 years were recruited from the Montréal metropolitan area. Absolute and relative agreements between methods were assessed. Vitamin K intakes measured by the VK‐FFQ (mean ± SD; 222 ± 186 μg/d) were 2.26 times (95%CI = 1.90, 2.67) higher than those obtained by FRs (135 ± 153 μg/d; P < 0.001). However, cross‐classification showed that 72% of participants were correctly classified into thirds and only 8% were grossly classified. Kappa value (κ=0.60) and correlation coefficient (r=0.83) also indicated a good relative agreement. In light of these results, the VK‐FFQ developed is a valid tool for ranking individuals according to their VK intakes. The poor absolute agreement likely results from the inability for FRs to adequately measure the usual intake of episodically consumed foods. The VK‐FFQ will be useful in large‐scale, population‐based research on VK and diseases as well as in clinical practice, especially that focusing on anticoagulant therapy. This research was supported by the Canadian Institutes of Health Research.
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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 | 0.009 | 0.012 |
| 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 | 0.000 |
| Open science | 0.001 | 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".