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Enregistrement W2560581721 · doi:10.1097/01.xeb.0000511332.11553.51

Predicting risk and outcomes for frail older adults

2016· article· en· W2560581721 sur OpenAlexaboutno aff
João Apóstolo, Elzbieta Bobrowicz‐Campos, Carol Holland, Richard Cooke, Sol Silva Santana, Maura Marcucci, Miriam Vollenbroek-Hutten, Federico Germini, Antonio Cano

Notice bibliographique

RevueInternational Journal of Evidence-Based Healthcare · 2016
Typearticle
Langueen
DomaineMedicine
ThématiqueFrailty in Older Adults
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMedicineContext (archaeology)GerontologyStressorQuality of life (healthcare)Health careMEDLINESystematic reviewVulnerability (computing)Psychiatry

Résumé

récupéré en direct d'OpenAlex

Background: Frailty is an age-related state of high vulnerability to adverse health outcomes after a stressor event, predisposing individuals to progressive decline in different functional domains and contributing to the onset of geriatric syndromes. Several assessment tools had their psychometric properties analysed in different systematic reviews (SRs). This study syntheses the existing evidence through an umbrella review (UR), developed within the context of the project “664367/FOCUS” funded under the European Union's Health Programme (2014–2020). Objective: To summarize the best available evidence from SRs on available measures to identify frailty in older adults, focusing on (i) their reliability, validity, and diagnostic accuracy in detecting the frail condition; (ii) their ability to predict adverse health outcomes. Methods: The review process was based on Joanna Briggs Institute procedures regarding URs. The studies considered as eligible for inclusion were quantitative SRs including older adults aged 60 years or more, recruited from any type of setting (primary care, long-term residential care, hospitals). The index tests were currently available screening tools for frailty in older adults. In addition, frailty indicators were considered. Tests from the Cardiovascular Health Study and the Canadian Study of Health and Aging, as well as other recognised gold standards were used as reference tests. Diagnosis of interest was frailty. Databases were searched from January 2001 to October 2015. Results: 10 SRs were appraised for methodological quality, and 6 SRs describing 25 screening tools and 7 frailty indicators were included. Based on evidence regarding capacity to detect frail condition, gait speed, Screening Letter, Timed get-up-and-go Test and PRISMA 7 appeared as potentially relevant for screening for frailty in a primary care setting. Tilburg Frailty Indicator was revealed to be the most reliable and valid measure (acceptable internal consistency, inter-rater reliability and concomitant validity). Frailty Index and two frailty indicators (gait speed and physical activity) were shown to be the most powerful predictors of future adverse health outcomes. Discussion: The most frequent limitations of 10 SRs were related to inappropriate definition of inclusion criteria, lack of a reference standard and lack of, or inappropriate tool used for critical appraisal of the included studies. In addition, lack of uniformity of provided statistics, and inconsistency in conferring significance to obtained results were observed. Almost all frailty indicators (with exception of gait speed) were not operationalized. Evidence compiled by this UR suggests that gait speed is the only measure that is sufficiently sensitive to identify frailty in older adults and, simultaneously, sufficiently accurate to predict increased risk of adverse outcome. However, because of limited specificity, its use in routine care should be accompanied by other instruments. Conclusion: The evidence showed that none of the analysed index tests has enough quality to be used as a single screening tool. There is a need for user-friendly instruments to identify frailty in older adults. Regarding predictive ability, Frailty Index, gait speed and physical activity can be used as single measures. Future studies focused on instruments for frailty should be more rigorous on methodology to improve the quality of obtained evidence.

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)0,006
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: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,222
Score d'incertitude au seuil0,753

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,006
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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 Opus0,078
Tête enseignante GPT0,386
Écart entre enseignants0,309 · 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

Citations1
Publié2016
Routes d'admission1
Résumé présentoui

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