Notice bibliographique
Résumé
Asia is the most populous region in the world and its rapidly growing societies are the sources of global development. However, aging of its population with increasing occurrence of diseases, of which dementia is the most prominent, is a major challenge to healthcare system. For example, there are 177 million people aged 65 and older living with dementia in China, expressing 20% of dementia patients worldwide. It has been estimated that the Chinese proportion of the elderly will reach 30.4% in 2050, which will include 100 million elderly people over 80 years of age [1]. Dementia prevalence in Asia, however, has previously been found to be lower than in Western populations [2]. Cultural differences could contribute to this. Dementia research in Chinese population has been primarily focused on Alzheimer's disease and vascular dementia [2]. Most of our knowledge about dementia, however, comes from studies in Caucasian population. Early and accurate diagnosis of dementia is crucial in order to start with the treatment as early as possible. Intervention and treatment of dementia (AD-dementia) can be cost-effective, but the majority of patients are not diagnosed in a timely manner. Technology is now available that can enable earlier detection of cognitive loss associated with incipient dementia, offering the potential for earlier intervention and health care systems and resulting in a less financial burden to an individual and a society. In this special issue on screening of dementia, we focused on screening tests for the detection of very mild dementia. We have invited a few papers that address those topics accordingly. One paper of this special issue gives a view on dementia screening in an outpatient department of a regional hospital in Taiwan by means of AD8 (ascertainment of dementia 8), a brief informal interview to screen dementia [3]. Screening people at the risk of dementia is a first and a major issue in screening of dementia. Another paper explores depression as a crucial public health issue in Taiwan. By means of brief tool, Epidemiological Studies Depression Scale (CES-D), to screen depression, a ratio of 16.4% of suspected depression patients compared to 13.3% aged patients out of all recruited patients was shown. This result may provide important information for a public health issue. In another paper, by means of AD8 consistent problems with thinking and/or memory were found in 56.8% participants, difficulty in remembering appointments was found in 47% participants, forgetting correct month or year was found in 40.9% of recruited participants in Taiwan, accordingly. Another paper presents the utility of informant AD8 for case finding of cognitive impairment in primary healthcare setting in Singapore. On a sample of 205 patients and their informants, AD8 was shown to be useful for case finding of cognitive impairment in the primary healthcare in one-third of adult patients. Another paper shows efficacy of the Takeda Three Colors Combination (TTCC) test, a screening tool for detection of very mild AD-dementia in Japan. Despite a lower sensitivity, a TTCC test was accomplished within 2 minutes in all subjects, thus being a great potential for the use as an AD screening tool by general practitioners in communities worldwide. Another paper assesses the influence of education on the performance of Chinese version of the Montreal Cognitive Assessment (C-MoCA) compared to Mini-Mental State Examination (MMSE) in detecting amnestic mild cognitive impairment (aMCI) among rural population in Beijing community. C-MoCA showed modest accuracy and was no better than MMSE in detecting aMCI, most likely due to overwhelming effect of education relative to aMCI diagnosis on variations in C-MoCA performance. Finally, all presented cognitive tests show great potential for the use as screening tools for early and very mild dementia worldwide, being easy to apply and at a low cost in communities worldwide. Rajka M. Liscic Gorsev G. Yener Huali Wang Jong-Ling Fuh Jianjun Jia Yuan-Han Yang
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 | 0,002 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,004 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,028 | 0,013 |
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 ».