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Validation of a semiquantitative food‐frequency questionnaire measuring dietary vitamin K intake in elderly

2008· article· en· W21081605 on OpenAlexafffundabout
Nancy Presse, Bryna Shatenstein, Marie‐Jeanne Kergoat, Guylaine Ferland

Bibliographic record

VenueThe FASEB Journal · 2008
Typearticle
Languageen
FieldNursing
TopicVitamin K Research Studies
Canadian institutionsUniversité de MontréalInstitut Universitaire de Gériatrie de Montréal
FundersCanadian Institutes of Health Research
KeywordsFood frequency questionnaireMedicineVitaminQuartileCohen's kappaDemographyEnvironmental healthInternal medicineStatisticsMathematics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.220
Threshold uncertainty score0.426

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.071
GPT teacher head0.306
Teacher spread0.235 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2008
Admission routes3
Has abstractyes

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