Perspectives on the representation of frailty in the electronic frailty index
Notice bibliographique
Résumé
BACKGROUND: Frailty is a state of increased vulnerability from physical, social, and cognitive factors resulting in greater risk of negative health-related outcomes and increased healthcare expenditure. A 36-factor electronic frailty index (eFI) developed in the United Kingdom calculates frailty scores using electronic medical record data. There is currently no standardization of frailty screening in Canadian primary care. In order to implement the eFI in a Canadian context, adaptation of the tool is necessary because frailty is represented by different clinical terminologies in the UK and Canada. In considering the promise of implementing an eFI in British Columbia, Canada, we first looked at the content validation of the 36-factor eFI. Our research question was: Does the eFI represent frailty from the perspectives of primary care clinicians and older adults in British Columbia? METHODS: A modified Delphi using three rounds of questionnaires with a panel of 23 experts (five family physicians, five nurse practitioners, five nurses, four allied health professionals, four older adults) reviewed and provided feedback on the 36-factor eFI. These professional groups were chosen because they closely work as interprofessional teams within primary care settings with older adults. Older adults provide real life context and experiences. Questionnaires involved rating the importance of each frailty factor on a 0-10 scale and providing rationale for ratings. Panelists were also given the opportunity to suggest additional factors that ought to be included in the screening tool. Suggested factors were similarly rated in two Delphi rounds. RESULTS: Thirty-three of the 36 eFI factors achieved consensus (> 80% of panelists provided a rating of ≥ 8). Factors that did not achieve consensus were hypertension, thyroid disorder and peptic ulcer. These factors were perceived as easily treatable or manageable and/or not considered reflective of frailty on their own. Additional factors suggested by panelists that achieved consensus included: cancer, challenges to healthcare access, chronic pain, communication challenges, fecal incontinence, food insecurity, liver failure/cirrhosis, mental health challenges, medication noncompliance, poverty/financial difficulties, race/ethnic disparity, sedentary/low activity levels, and substance use/misuse. There was a 100% retention rate in each of the three Delphi rounds. CONCLUSIONS AND NEXT STEPS: Three key findings emerged from this study: the conceptualization of frailty varied across participants, identification of frailty in community/primary care remains challenging, and social determinants of health affect clinicians' assessments and perceptions of frailty status. This study will inform the next phase of a broader mixed-method sequential study to build a frailty screening tool that could ultimately become a standard of practice for frailty screening in Canadian primary care. Early detection of frailty can help tailor decision making, frame discussions about goals of care, prevent advancement on the frailty trajectory, and ultimately decrease health expenditures, leading to improved patient and system level outcomes.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,079 | 0,081 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,008 | 0,013 |
| Communication savante | 0,008 | 0,005 |
| Science ouverte | 0,002 | 0,008 |
| Intégrité de la recherche | 0,003 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 source (Gemma direct ou Codex distillé), 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 ».