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Enregistrement W4412853431 · doi:10.1002/jper.11378

Multi‐cohort evaluation of “Don't know” responders to self‐report oral health questions: Implications for etiologic research

2025· article· en· W4412853431 sur OpenAlexaboutno aff
Julia C. Bond, Mabeline Velez, Sharon M. Casey, Lauren A. Wise, Yvette C. Cozier, Matthew P. Fox, Raul I. García, Brenda Heaton

Notice bibliographique

RevueJournal of Periodontology · 2025
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueSurvey Methodology and Nonresponse
Établissements canadiensnon disponible
Organismes subventionnairesEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Center for Advancing Translational SciencesNational Institute of Dental and Craniofacial ResearchNational Institute of Child Health and Human DevelopmentNational Cancer InstituteNational Institutes of Health
Mots-clésMedicineCohortObservational studyNational Health and Nutrition Examination SurveyCohort studyFamily medicinePopulationEducational attainmentOral healthPeriodontitisDemographyEnvironmental healthGerontologyDentistry

Résumé

récupéré en direct d'OpenAlex

BACKGROUND: Self-reported data can extend the reach of oral health research, but "Don't know" responses may threaten validity. We explored characteristics of participants who responded "Don't know" to a periodontal health question across three distinct cohorts. METHODS: We used data from three questionnaire-based observational studies, namely, the Pregnancy Study Online (PRESTO) (N = 10,996), the Black Women's Health Study (BWHS) (N = 479), and the National Health and Nutrition Examination Survey (NHANES) (N = 15,502), to evaluate responses to questionnaire items related to periodontal health (e.g., "Has a dentist or dental hygienist ever told you that you have periodontitis or gum disease?"). We compared sociodemographic and behavioral factors across each response category ("Yes," "No," "Don't know"). We used Monte Carlo simulation to create multiple datasets of 100,000 participants under different scenarios to calculate the percent change in observed effect estimates in analyses using the full cohort compared to analytic cohorts excluding "Don't know" respondents. RESULTS: "Don't know" prevalences ranged from 1.6% to 4.1%. We observed differences between "Don't know" responders and those who answered "Yes" or "No" across all three cohorts. "Don't know" responders were more likely to have lower educational attainment, lower income, and reduced engagement with oral healthcare services. We observed substantial bias in complete-case effect estimates in some simulated scenarios. Bias was larger when the underlying population prevalence of "Don't know" responses was higher. CONCLUSIONS: "Don't know" responders had distinct patterns of sociodemographic characteristics and oral healthcare engagement. The degree of bias in complete-case analysis was dependent on simulated factors. PLAIN LANGUAGE SUMMARY: Research about oral health often asks people to answer questions about their teeth and gums. Sometimes people respond that they "Don't know" the answer to these questions, which can make data challenging for researchers to analyze. In this study, we used three different data sources to look at whether there were particular characteristics that were more common among people who said they "Don't know" in response to a question about their gum health. "Don't know" responses were not very common in any of the three groups, ranging from 1.6% in a representative survey of people in the United States to 4.1% in a group of women in the United States and Canada trying to become pregnant. In all three groups, people who said "Don't know" had a lower household income, less education, and were less likely to have seen a dentist recently. We also used simulated datasets to evaluate when excluding people who responded "Don't know" would be expected to cause the most bias in analyses. The expected bias increased with the number of "Don't know" responses in the data.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,199
score de la tête « metaresearch » (Gemma)0,105
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche
Catégories consensuellesMétarecherche
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,129
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,1990,105
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,601
Tête enseignante GPT0,654
Écart entre enseignants0,053 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission1
Résumé présentoui

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