Associations between xerostomia and health status indicators in the elderly
Notice bibliographique
Résumé
AIMS: This study investigated the associations between xerostomia (dry mouth) (low, moderate and high) with other categorical variables (e.g. demographic and health status indicators). This paper aims to report on the severity of xerostomia in the elderly population and investigate the relationship with other aspects of perceived health. METHOD: Data were obtained from a cross-sectional survey of 225 elderly people from a large multilevel geriatric care centre. The centre consists of three levels of care: an apartment building in which residents live more or less independently, a home for the aged, and a chronic care hospital. Participants in the study were recruited when they attended the dental care facility. Data were collected by means of a personal interview conducted either at the dental care facility or the participant's residence. RESULTS: The mean age was 83 years. Most were females (72%) and almost all (99%) reported one or more chronic medical conditions; 88% had physical disabilities. Xerostomia was recorded on a seven-point scale. Scores were categorised as low, medium or high and the proportions were 49.3%, 30.3% and 20.4% respectively Bivariate analysis showed no association between dry mouth and sex, age, general health change or life satisfaction. However, when the high xerostomia group was separated out and odds ratios calculated they were 2.3 to 4.9 times more likely to experience a negative impact on health than the low group. Xerostomia did not have a significant impact on chewing capacity, morale or stress, although it contributed to the variability of the oral health-related quality of life measures. It was the only variable with a significant effect (OR 2.55) for the Oral Health Impact Profile-14 and displayed a higher odds ratio (2.76) for the Geriatric Oral Health Assessment Index. Self-reported xerostomia in the elderly population can be categorised into a severity scale. Those suffering most from xerostomia are more likely to experience a negative impact on general health. CONCLUSION: The key finding in this study is that xerostomia has a significant and negative impact on the quality of life of elderly individuals, though oral function may be less affected.
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 | 0,005 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 ».