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Enregistrement W7073682715

Predicting risk and outcomes for frail older adults:an umbrella review of frailty screening tools

2017· article· en· W7073682715 sur OpenAlexaboutno aff

Notice bibliographique

RevueAston Publications Explorer (Aston University) · 2017
Typearticle
Langueen
DomaineEconomics, Econometrics and Finance
ThématiqueDiverse Scientific and Economic Studies
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésChecklistData extractionSystematic reviewMEDLINEMeta-analysisRisk assessmentFrailty Index
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Background: A scoping search identified systematic reviews on diagnostic accuracy and predictive ability of frailty measures in older adults. In most cases, research was confined to specific assessment measures related to a specific clinical model. Objectives: To summarize the best available evidence from systematic reviews in relation to reliability, validity, diagnostic accuracy and predictive ability of frailty measures in older adults. Inclusion criteria population: Older adults aged 60 years or older recruited from community, primary care, long-term residential care and hospitals. Index test: Available frailty measures in older adults. Reference test: Cardiovascular Health Study phenotype model, the Canadian Study of Health and Aging cumulative deficit model, Comprehensive Geriatric Assessment or other reference tests. Diagnosis of interest: Frailty defined as an age-related state of decreased physiological reserves characterized by an increased risk of poor clinical outcomes. Types of studies: Quantitative systematic reviews. Search strategy: A three-step search strategy was utilized to find systematic reviews, available in English, published between January 2001 and October 2015. Methodological quality: Assessed by two independent reviewers using the Joanna Briggs Institute critical appraisal checklist for systematic reviews and research synthesis. Data extraction: Two independent reviewers extracted data using the standardized data extraction tool designed for umbrella reviews. Data synthesis: Data were only presented in a narrative form due to the heterogeneity of included reviews. Results: Five reviews with a total of 227,381 participants were included in this umbrella review. Two reviews focused on reliability, validity and diagnostic accuracy; two examined predictive ability for adverse health outcomes; and one investigated validity, diagnostic accuracy and predictive ability. In total, 26 questionnaires and brief assessments and eight frailty indicators were analyzed, most of which were applied to community-dwelling older people. The Frailty Index was examined in almost all these dimensions, with the exception of reliability, and its diagnostic and predictive characteristics were shown to be satisfactory. Gait speed showed high sensitivity, but only moderate specificity, and excellent predictive ability for future disability in activities of daily living. The Tilburg Frailty Indicator was shown to be a reliable and valid measure for frailty screening, but its diagnostic accuracy was not evaluated. Screening Letter, Timed-up-and-go test and PRISMA 7 (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) demonstrated high sensitivity and moderate specificity for identifying frailty. In general, low physical activity, variously measured, was one of the most powerful predictors of future decline in activities of daily living. Conclusion: Only a few frailty measures seem to be demonstrably valid, reliable and diagnostically accurate, and have good predictive ability. Among them, the Frailty Index and gait speed emerged as the most useful in routine care and community settings. However, none of the included systematic reviews provided responses that met all of our research questions on their own and there is a need for studies that could fill this gap, covering all these issues within the same study. Nevertheless, it was clear that no suitable tool for assessing frailty appropriately in emergency departments was identified.

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)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,678
Score d'incertitude au seuil0,741

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0010,000
Communication savante0,0000,002
Science ouverte0,0010,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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 Opus0,104
Tête enseignante GPT0,262
Écart entre enseignants0,158 · 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; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
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
Publié2017
Routes d'admission1
Résumé présentoui

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