Self- and Informant-Report Cognitive Decline Discordance and Mild Cognitive Impairment Diagnosis
Notice bibliographique
Résumé
Importance: Subjective report of cognitive and functional decline from participant-study partner dyads can efficiently assess risk of cognitive impairment and clinical progression. Accuracy of self-report subjective cognitive decline may be limited by lack of awareness about one's own cognitive abilities in adults with MCI and dementia, and the extent to which discordance between self- and study partner-report is associated with diagnosis of cognitive impairment is unknown. Objective: To investigate the association between discordance between self- and study partner-reported cognitive and/or functional decline and MCI diagnosis. Design, Setting, and Participants: This multisite, cross-sectional study used baseline data from 2 longitudinal, observational studies. A total of 921 participant-study partner dyads enrolled in the Alzheimer Disease Neuroimaging Initiative (ADNI) from December 2016 to July 2022, and 279 dyads enrolled in the Brain Health Registry Electronic Validation of Online Methods Study (eVAL) from January 2020 to July 2023 were included. Exposures: Participants and study partners completed the Everyday Cognition Scale (ECog). Participants completed a demographics survey and the Geriatric Depression Scale-Short Form (GDS). Main Outcomes and Measures: The model selection procedure in ADNI identified variables, which were included in a model that was externally validated in the eVAL cohort. The primary outcome was MCI vs cognitively unimpaired (CU) among participants. Results: ADNI participants (921 dyads) had a mean (SD) age of 71 (7) years and mean (SD) of 17 (3) years of education; 485 (53%) were female, 30 (3%) were Asian, 105 (11%) were Black, and 756 (82%) were White. eVAL participants (279 dyads) had a mean (SD) age of 71 (8) years and mean (SD) of 17 (2) years of education; 151 (54%) were female, 17 (6%) were Asian, 12 (4%) were Black, and 245 (88%) were White. The model distinguished CU vs MCI in the validation cohort with an area under the curve of 0.87 (95% CI, 0.88-0.96), sensitivity of 0.50 (95% CI, 0.49-0.80), and specificity of 0.97 (95% CI, 0.95-0.99) based on a regression model. The model included 4 discordance metrics, participant demographics (gender, age, and education), study partner demographics (gender and cohabitation), and depressive symptoms (GDS score). Conclusions and Relevance: In this cross-sectional study of 1200 dyads, measures of ECog score discordance helped distinguish CU from MCI individuals with high specificity. Participant and study partner agreement on lack of observed changes in the participant was associated with lower likelihood of MCI, highlighting the value of dyadic discordance metrics for ruling out MCI in diverse settings.
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 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| 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 ».