Risk Indicators for Periodontal Disease in a Remote Canadian Community—a Dental Practice‐based Study
Notice bibliographique
Résumé
OBJECTIVES: The purpose of this cross-sectional study was to identify risk markers and risk indicators for periodontal attachment loss in a remote Canadian community. Of special interest was the association between smoking and periodontal disease experience. METHODS: Data were collected from a convenience sample of 187 adult patients attending a dental office in a rural community located in Northern Ontario. Information was obtained via a questionnaire and a periodontal examination. The questionnaire included the use of dental services, self-care behaviors, general health status, smoking, and personal characteristics. Periodontal health was assessed using the mean periodontal attachment loss (MPAL), measured at two sites on all remaining teeth and the proportions of sites examined with loss of 2 mm or more and 5 mm or more. Plaque scores and measures of the number of missing teeth also were obtained. The relationships between mean periodontal attachment loss, the proportion of sites with 5 mm or more of loss and independent variables such as age, sex, current smoking status, mean tooth plaque scores, flossing frequency, and regularity of preventive dental visits were examined in bivariate and multivariate analyses. RESULTS: The data revealed a mean periodontal attachment loss of 3.9 mm (SD=1.5). The mean proportion of sites examined with loss of 2 mm or more was 0.89 and the mean proportion with loss of 5 mm or more was 0.35. In linear regression analysis, plaque scores, the number of missing teeth, age, current smoking status, regularity of dental visits, and flossing frequency had statistically significant independent effects and explained 60.0 percent of the variance in mean periodontal attachment loss. Just over 30 percent of subjects had severe periodontal disease, defined as 50 percent or more of sites examined with loss of 5 mm or more. In logistic regression analysis, missing teeth, dental visiting, smoking status, age, and flossing frequency had significant independent effects. The strongest association observed was with smoking, which had an odds ratio of 6.3. The logistic regression model correctly predicted 64.3 percent of cases with severe disease. CONCLUSIONS: The data indicate that the periodontal health of these patients is poor. Risk indicators or markers of poor periodontal health in the population studied included missing teeth, plaque scores, age, current smoking status, regularity of dental visits, and flossing frequency. This supports previous findings that behavioral factors play an important role in periodontal disease.
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 | 0,007 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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.
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 ».