P3‐074: Frailty as a risk for the development and progression of cognitive impairment in older adults: Results of a dynamic model
Notice bibliographique
Résumé
With age, people accumulate a variety of deficits, including physical and cognitive ones. The prevalence of cognitive impairments dramatically increases with age, as does the prevalence of frailty. Here we considered how cognitive deficits, operationalized as a continuum of cognitive test score errors, accumulate in relation to frailty, operationalized across a continuum of non-cognitive (physical, functional) deficits, in relation to age, sex and education. Five-year changes in cognition (defined as the errors on the Modified Mini-Mental State Examination) were analyzed in relation to general health status (defined by the Frailty Index) in older Canadians (n = 8,403). A Markov chain was used to model the probabilities of changes in cognitive test scores. A generalized linear model and logistic regression were used to estimate change in cognition and mortality risks, respectively. Baseline cognition, age, frailty, sex and education were covariates. Age and frailty were considered both as continuous variables and dichotomized at their median values. Age and frailty were each consistently associated both with cognitive changes and with the risk of death. Education main effects were significantly associated with cognitive transitions, but not with mortality. Sex was associated only with mortality. Frail people less often showed cognitive improvement or stabilization (22.3%, 95% CI = 20.1%-24.5%) compared with non-frail people, of whom 40.9% (95% = 39.7%-42.1%) did not deteriorate. Similarly, frail people were more likely to die (47.4%, 95%CI = 44.8%-50%) versus 22.3% (95% CI = 20.1%-24.5%) of those not frail. Although education did not influence mortality, people with higher education had a greater chance of cognitive stabilization or improvement: among more educated people 39.9% improved or remained stable (95% CI = 38.4%-41.4%) than did less educated people (33.2%, 95%CI = 31.7%-34.7%). Frailty was a risk for cognitive decline. In contrast to other approaches, our model makes it possible to analyze not only regression (average effects) but calculates the likelihood of changes in all direction including improvement. To now, cognitive impairment is usually considered as measurement error; our data suggest that it is real and predictable. It appears that the ability to fight back is an intrinsic property, even in people affected by at least the early stages of ‘irreversible’ illnesses such as dementia.
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 | 0,007 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,005 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,004 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,014 | 0,001 |
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 ».