Erosive Tooth Wear among Adults in Lithuania: A Cross-Sectional National Oral Health Study
Notice bibliographique
Résumé
INTRODUCTION: Erosive tooth wear has a multifactorial origin, where multiple risks contribute to its initiation and subsequent progression. The prevalence of tooth wear varies among countries; therefore, national studies are needed to examine the prevalence of this condition and its associated determinants. MATERIALS AND METHODS: A sample of this national study included a total of 1,397 adults (response rate of 52%). Severity and number of teeth with erosive tooth wear, caries experience (D3MFS), and fluorosis were assessed clinically. A self-reported questionnaire inquired about sociodemographics, oral health behavior, diet, and general health. Fluoride levels in drinking water at the recruitment areas were also recorded. Data were analyzed by bivariate and multivariate methods. RESULTS: The prevalence of erosive tooth wear in enamel and dentin combined was 59% among 35- to 44-year-old, 75% among 45- to 54-year-old, 70% among 55- to 64-year-old, and 66% among 65- to 74-year-old males. The prevalence among females in the respective age groups was 44, 60, 63, and 59%. Erosive tooth wear in enamel was associated with a lower fluoride level (≤1 ppm) in the drinking water (OR 2.1, 95% CI 1.1-4.2). Erosive tooth wear in dentin was positively associated with male gender (OR 1.7, 95% CI 1.1-2.5), periurban/rural residency (OR 1.6, 95% CI 1.1-2.4), older age (OR 1.6, 95% CI 1.3-1.9), presence of reflux (OR 3.3, 95% CI 1.0-10.9), and negatively with higher D3MFS scores (OR 0.7, 95% CI 0.5-0.9). CONCLUSIONS: The prevalence of erosive tooth wear in enamel and dentin was relatively high in Lithuania; the erosive tooth wear in enamel and dentin combined was 52% among 35- to 44-year-olds, 68% among 45- to 54-year-olds, 67% among 55- to 64-year-olds, and 63% among 65- to 74-year-olds. Lower fluoride level in drinking water was associated with erosive tooth wear in enamel. Male gender, residency in periurban/rural areas, older age, and presence of acid reflux were associated with higher odds, while higher D3MFS scores were associated with lower odds for erosive tooth wear in dentin. These results can be used to plan dental public health prevention.
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 enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,001 |
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 tête enseignante, 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 ».