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Enregistrement W2619758264 · doi:10.1097/01.hj.0000520664.00897.9b

Cognitive Screening Model Expands Health Care Delivery

2017· article· en· W2619758264 sur OpenAlexaboutno aff
Oscar Armero, Sarah Crosson, Amber Kasten, Victoria Martin, Craig Spandau

Notice bibliographique

RevueThe Hearing Journal · 2017
Typearticle
Langueen
DomaineNeuroscience
ThématiqueHearing Loss and Rehabilitation
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésCognitionDementiaHearing lossCognitive declineMedicineAudiologyGerontologyDiseasePsychologyClinical psychologyPsychiatry

Résumé

récupéré en direct d'OpenAlex

Figure: iStock/AlexRathsAge-related cognitive decline can negatively impact many aspects of hearing health care, including assessment, counseling, intervention, and outcome. Cognitive impairment can range from mild to severe, and affects memory, learning, and concentration. These types of impairments in older adults are often a result of medication side effects, disruption of normal metabolic and/or endocrine processes, illness, depression, and dementia. Approximately 5.1 million Americans 65 years or older have Alzheimer's disease, which is one of the most well-known forms of cognitive impairment. According to the Centers for Disease Control and Prevention (CDC), only 19 percent of adults aged 60 and older report memory loss or confusion to health care providers (CDC & Alzheimer's Assoc, 2013 http://bit.ly/2oFljvK). A literature review by Beck et al. suggests that administration of cognitive screening tools and appropriate referrals are important components when evaluating older adults (Hearing Rev. 2016:23[2]:36). HEARING LOSS & COGNITIVE DECLINE Frank Lin, MD, PhD, and his colleagues at Johns Hopkins University found an association between greater hearing loss and lower scores on executive function and psychomotor processing testing (Arch Neurol. 2011:68[2]: 214 http://bit.ly/2on896O). They also associated greater hearing loss with lower scores on measures of mental status, memory, and executive function. These findings were supported by Taljaard, et al. who added that the degree of cognitive deficit is significantly associated with the degree of hearing impairment (Clin Otolaryngol. 2016:41[6]:718 http://bit.ly/2on1qtB). Many individuals in the early stages of cognitive decline are undiagnosed, and audiologists may be the first to encounter patients yet to be identified. Given the high correlation between hearing loss and cognitive decline, a screening and intervention model was created and successfully piloted to address the needs of those who may otherwise be missed. This model could serve as an example for other hearing health professionals to expand and improve health care delivery. Patients are mailed a case history form with a notification of their appointment that includes a question on their memory. If there are concerns that a memory problem may exist, the patient and his/her significant other are asked to describe the concerns at the appointment with the audiologist. Screening tests are often used to provide an indication of whether there is cognitive impairment, and can be very useful, provided their limitations are clearly understood (Handbook of The Clinical Psychology of Aging, 2008). The Patient Protection and Affordable Health Care Act of 2010 mandated this screening, which can be performed by many licensed and trained health care professionals, and is endorsed by many organizations including the American Speech-Language-Hearing Association. COGNITIVE SCREENING TOOLSFigure 1: The Saint Louis University Mental Status (SLUMS) screening form (Am J Geriatr Psychiatry. 2006;14[11]:900 http://bit.ly/2ofzsOF).Historically, the Mini-Mental State Exam (MMSE) and the Montreal Cognitive Assessment (MoCA) have been the primary cognitive screening tools. Although these screenings have a high sensitivity and specificity for detecting cognitive decline, they can be extensive, which poses a challenge in a busy clinic. Alternatively, the Saint Louis University Mental Status (SLUMS) exam, an 11-item cognitive assessment, can be administered in less than 10 minutes (Fig. 1). This tool may be more sensitive than the MMSE and has similar evidence of validity to the MoCA. This scale is based on a clinical study performed and validated at four VA medical centers in 2007. It was developed in partnership with the Geriatrics Research, Education, and Clinical Center (GRECC) at the St. Louis Veterans Administration Medical Center and validated in 2006. The SLUMS examines the following cognitive domains: orientation, recall, attention, calculation, language, constructional praxis, and fluency. An informational video on administering the SLUM can be found online http://bit.ly/2ofDNBf. Patients who fail the SLUMS at our clinic are referred to a specialized memory clinic that provides same-day services, including a neuropsychological interview, testing, scoring, interpretation, counseling, intervention planning, and when appropriate, a referral to a psychiatrist for medical management. Memory care workshops and support groups are also available to family members or individuals caring for the patient. These programs are led by a licensed social worker who provides an overview of the disease, as well as information about planning, home safety, resources, self-care, and compassionate communication activities. SUCCESS STORY Screenings can identify signs of cognitive decline and lead to early intervention. This is illustrated in the case of a 75-year-old man first seen in October 2013 at our audiology clinic. He was referred to the clinic for an audiologic evaluation of suspected hearing loss and recent onset vertigo. At the initial consultation, the patient referred to his spouse to answer many of the questions for him. This action prompted concern that he may have a cognitive problem, and when this was mentioned, both the patient and his spouse expressed similar concerns. The spouse admitted that she compensated by accompanying the patient to all his appointments. The patient agreed to take the SLUMS exam and scored 15 out of 30, which indicates a positive finding for possible dementia. The patient was referred to the memory clinic, where a neuropsychologist diagnosed a major neurocognitive disorder consistent with Alzheimer's disease. The patient was then referred to a psychiatrist, who diagnosed him to have early-stage Alzheimer's and prescribed donepezil and mirtazapine. The patient was enrolled in an educational program to learn compensatory strategies, and his spouse joined the memory care workshop. The patient has been monitored biannually by psychiatrists and the latest report, written in January 2017, stated that he remains independent with little change in functioning. This patient's case shows the value of early diagnosis of cognitive impairment. Providing a top-down auditory assessment and intervention program is a patient-centered example of delivering better care to elderly and vulnerable patients and their families. This pilot program has functioned well for several years, and can be easily incorporated into hearing health care practices.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesÉtudes des sciences et des technologies
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Autre devis · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,783
Score d'incertitude au seuil0,997

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0040,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,135
Tête enseignante GPT0,374
Écart entre enseignants0,239 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeAutre devis
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations2
Publié2017
Routes d'admission1
Résumé présentoui

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