Application of Screening Scale for Mild Cognitive Impairment in screening mild cognitive impairment of the elderly in rural communities in Hangzhou, Zhejiang
Notice bibliographique
Résumé
Objective To investigate the prevalence of mild cognitive impairment (MCI) among the elderly in rural communities in Hangzhou, Zhejiang, and to explore the screening accuracy of Screening Scale for Mild Cognitive Impairment (sMCI) in the elderly with low education. Methods From April 2010 to September 2010, 360 elderly people in Sijiqing street, Jianggan district (now Shangcheng district), Hangzhou, Zhejiang were recruited. Dementia and MCI were judged by Mini⁃Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), sMCI and Clinical Dementia Rating Scale (CDR). Draw receiver operating characteristic (ROC) curve and calculate the area under the curve (AUC), and compare the accuracy of sMCI, MoCA and CDR scores in screening MCI. Results Finally, 171 cases completed all investigations. 1) 55 cases (32.16%) were diagnosed as MCI, including 25 cases (14.62%) of amnestic MCI (aMCI) and 30 cases of non⁃aMCI, 11 cases (6.43%) of dementia, 10 cases (5.85%) of depression, 4 cases (2.34%) of anxiety disorder, one case (0.58%) of bipolar disorder, one case (0.58%) of schizophrenia and one case (0.58%) of mental retardation. Among 154 patients with cognitive impairment, 25 cases (16.23%) were screened for dementia by MMSE, 8 cases (5.19%) were screened for dementia by CDR, and 11 cases (7.14%) were clinically confirmed; 138 cases (89.61%) of MCI were screened by MoCA, 117 cases (75.97%) by sMCI, 70 cases (45.45%) by CDR, and 55 cases (32.16%) were clinically confirmed. 2) Taking clinical diagnosis as reference standard, the ROC curve showed CDR score had the highest accuracy in screening MCI, and the AUC was 0.90 ± 0.03 (95%CI: 0.844-0.957, P=0.000); the AUC of MoCA score was 0.53 ± 0.05 (95%CI: 0.430-0.621, P=0.603); when the cut⁃off value of sMCI score was 23, the AUC was 1.00 ± 0.00 (95%CI: 1.000-1.000, P=0.000). The cut⁃off value of subjects with education level of 0-3 years was adjusted to 22, and the AUC was 0.67 ± 0.05 (95%CI: 0.578-0.756, P=0.001). 3) According to education level, they were divided into 0-3 years group (113 cases) and 4-6 years group (47 cases). Taking CDR score as the reference standard, ROC curve showed the AUC of MoCA score in screening MCI in 4-6 years group was 0.49 ± 0.17 (95%CI: 0.157-0.824, P=0.955), the cut⁃off value of sMCI score was 23, the AUC of sMCI score was 0.50 ± 0.17 (95%CI: 0.161-0.839, P=1.000); the AUC of MoCA score in the 0-3 years group was 0.51 ± 0.06 (95%CI: 0.402-0.617, P=0.858), and the cut⁃off value of sMCI score was adjusted to 22, and the AUC was 0.64 ± 0.05 (95%CI: 0.535-0.744, P=0.011). Conclusions It is more common for the elderly in the rural communities with low education to have MCI, the accuracy of sMCI in screening MCI is higher than MoCA, and the cut⁃off value is 23 (education level 4-6 years) and 22 (education level 0-3 years), which is worthy of clinical application.
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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 | 0,002 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».