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”)
Notice bibliographique
Résumé
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
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,018 | 0,112 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,013 | 0,003 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».