Considerations for Dietary Assessment in the Canadian Partnership for Tomorrow Project
Notice bibliographique
Résumé
Dietary factors are leading contributors to chronic disease and mortality globally and in Canada (1–3), and have been recognized as modifiable risk factors for certain cancers (4). However, much remains to be learned about how dietary factors interact with other modifiable and nonmodifiable exposures and physiologic variables to influence disease risk in humans (5,6). \nInformation collected from large prospective cohorts plays an important role in furthering our understanding of diet-disease relationships (7,8). To advance knowledge on how to promote health and prevent disease, it is critically important to use robust tools for collecting dietary information from participants in such cohorts (9). This guide is intended to be utilized by researchers designing nutritional epidemiological research and in particular, to guide the implementation of dietary assessment tools within the CPTP cohorts. The aim is to provide guidance on method selection, data collection, and analyses of dietary data, as well as stimulate discussions of harmonization of methods across cohorts to advance the evidence base. Because objective measures such as biomarkers of diet are currently few, burdensome, costly, and limited in the information they provide about the types of foods and beverages people consume (5,6), researchers typically rely upon self-report tools. However, it has long been recognized that self-reported dietary data are affected by error, including systematic error or bias (9,10), leading some commentators to suggest that research should no longer rely on selfreport approaches (11,12). However, much work has been conducted to better understand and address error in self-report dietary intake data (9,10). Such work has informed the development of novel technology-enabled tools to allow collection of the least-biased data possible, as well as the development of rigorous statistical approaches to mitigate the effects of error (13–16). Based on what is known about sources and types of error in data captured using different types of tools, it has been recommended that a combination of tools may be the optimal way forward for cohort studies. Specifically, multiple 24-hour recalls (24HRs), administered in combination with a food frequency questionnaire (FFQ), may allow researchers to leverage the strengths of each instrument (10,14,17). Data from 24HRs provide comprehensive detail on intake and measure consumption with less bias than FFQs. On the other hand, FFQs measure intake over a longer period (e.g. past month or year) (18–20), meaning they are better able to capture intake \nof foods and beverages that may be consumed more episodically (e.g., whole grains, dark-green vegetables) but that may be important to diet-disease relationships. The availability of weband mobile device-based dietary assessment tools for use in Canada and emerging statistical techniques to analyze the resulting data makes this multiple-tool scenario a realistic \nconsideration for Alberta’s Tomorrow Project (21), other cohorts within the Canadian Partnership for Tomorrow Project (CPTP) (22), and other health-related studies. With comprehensive and standardized measurement of dietary exposures across cohorts, the identification of promising strategies to reduce diet-related disease risk among Canadians can be furthered (9).
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 | 0,059 | 0,098 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,003 | 0,006 |
| Études des sciences et des technologies | 0,017 | 0,005 |
| Communication savante | 0,008 | 0,004 |
| Science ouverte | 0,008 | 0,010 |
| Intégrité de la recherche | 0,006 | 0,008 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,021 | 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 ».