269 Early Identification of Frailty: Developing an International Delphi Consensus for a Definition of Pre-frailty
Notice bibliographique
Résumé
Abstract Background Frailty is associated with a prodromal stage called pre-frailty, a potentially reversible and highly prevalent condition before frailty becomes established. Despite this, there is no widely accepted definition of pre-frailty to support its early identification and management. This study applied an international consensus approach to define and better understand pre-frailty. Methods A modified electronic two-round Delphi Consensus study was conducted. In all, 23 experts from 12 countries with different backgrounds participated. The questionnaire was developed following a systematic literature review. An online consensus meeting was conducted with eight Delphi participants and two external experts. Qualitative and quantitative methods were employed for data analysis. An agreement level of 70% was applied for accepting statements. Results A total of 71 statements were circulated in Round 1. Of these, 52.8% were accepted. Fifty-one statements were re-circulated in Round 2, of which 92.1% were accepted. The online consensus meeting produced a consensus statement describing the concept, multi-factorial nature, and mechanism of pre-frailty as well as assessment, prevention and management approaches. All experts agreed that physical and non-physical factors such as psychological and social capacity are involved in the development of pre-frailty, potentially adversely affecting health and health-related quality of life outcomes. Practitioners should regard pre-frailty as a multi-factorial, multi-dimensional, and non-linear process that does not inevitably lead to frailty. It might be reversed or attenuated by targeted interventions. Brief, feasible and validated tools are recommended for opportunistic screening or case-finding followed by confirmation with multi-dimensional assessment. Conclusion It is difficult to establish consensus on one compact definition of pre-frailty, which is a multi-dimensional concept not only associated with physical impairment, but also with cognitive, nutritional, socioeconomic and other aspects of frailty. However, it may be too early to agree on an operational definition of pre-frailty since none yet exists for frailty.
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 | 0,000 |
| 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 | 0,000 | 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 ».