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Record W4413888759 · doi:10.1093/aje/kwaf154

Differences between the Canadian and US Diet History Questionnaires and their updated versions. (Letter-to-the-editor re: “Agreement between the National Cancer Institute’s Diet History Questionnaire II and III in a preconception cohort”)

2025· article· en· W4413888759 on OpenAlexaffabout
Ilona Csizmadi, Beatrice A. Boucher, Vikki Ho, Jennifer E. Vena, Anita Koushik

Bibliographic record

VenueAmerican Journal of Epidemiology · 2025
Typearticle
Languageen
FieldMedicine
TopicNutritional Studies and Diet
Canadian institutionsMcGill University Health CentreAlberta Health ServicesUniversity of CalgaryCentre Hospitalier de l’Université de MontréalUniversity of TorontoUniversité de Montréal
Fundersnot available
KeywordsGerontologyMedicineFamily medicineLibrary scienceDemographyEnvironmental healthSociologyComputer science

Abstract

fetched live from OpenAlex

We read with interest the recent paper by Julian-Serrano et al.1 comparing the nutrient profiles from two food frequency questionnaires (FFQ), the NCI’s Diet History Questionnaire (DHQ) II and III, administered to a subgroup of Pregnancy Study Online (PRESTO) participants. As highlighted by the authors, FFQs occasionally need to be modified to reflect changing food environments and shifting trends in eating behavior. In longitudinal studies, where over time more than one version of an FFQ may be administered to best capture intakes, challenges may arise in the interpretation of results related to diet and health outcomes. Addressing this methodological concern entails having an in-depth understanding of the performance of updated FFQs relative to earlier versions. The results reported by Julian-Serrano et al.1 showing generally good to moderate reliability and agreement for most of the 30 nutrients compared between the DHQs is welcome news for studies faced with needing to use both versions over time. Given that PRESTO is a cross-border study that recruits participants from Canada and the United States2 (though only US residents were included in this reported reliability study), we would like to bring attention to the Canadian versions of these questionnaires (ie, C-DHQ II and C-DHQ III), available online alongside the US versions at the NCI website.3 Specifically, we highlight that although the USand Canadian DHQs are largely similar, the C-DHQ II is more closely aligned with the C-DHQ III than is the US DHQ II with the US DHQ III. As we previously reported4 and as shown on the NCI website,3 most of the additions and deletions attributed to the US DHQ III had already been implemented in the C-DHQ II, released at an earlier date. For example, various types of milk (eg, almond, rice, soy), milkshakes, vitamin water, espresso drink mixtures, artificial sweeteners (eg, stevia), and expanded fish list (eg, oily vs lean) were line items queried in the C-DHQ II,4 but items newly added to the US DHQ III (Julian-Serrano et al: Table S1).1 As reliability and agreement comparisons of the C-DHQ II and C-DHQ III have not yet been conducted, we caution investigators using the Canadian DHQs (recommended for studies with Canadian participants given differences in cross-border food markets and nutrient fortification practices4,5) not to assume that the nutrient profile differences and/or agreements reported by Julian-Serrano et al.,1 for the US versions necessarily apply to the C-DHQ II vs III. While the limited number of food list differences between the C-DHQ II and III may not substantially impact nutrient profiles—this remains to be studied. Of note, more important differences may be expected with food and nutrient comparisons between the C-DHQ I5 and subsequent C-DHQs II and III, which were more extensively revised and update to reflect contemporary Canadian food consumption patterns.4,6,-8 Hence, for clarity in the interpretation of results from studies using multiple versions of these questionnaires, it will be important to conduct validation and reliability studies. Modifying and evaluating the performance of updated FFQs in relation to previous versions can be a painstakingly laborious undertaking. The recognition by the research community that this is a worthwhile endeavor is an attestation to the continuing relevance and enduring utility of FFQs, as nicely described in other recent AJE papers.9,10

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.018
metaresearch head score (Gemma)0.112
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.586
Threshold uncertainty score0.823

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.112
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0130.003

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 head0.036
GPT teacher head0.305
Teacher spread0.268 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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
Published2025
Admission routes2
Has abstractyes

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