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The QuickSort: A brief screen for detecting cognitive impairment in older adults

2022· dissertation· en· W7061459045 sur OpenAlexaboutno aff

Notice bibliographique

RevueAdelaide Research & Scholarship (AR&S) (University of Adelaide) · 2022
Typedissertation
Langueen
DomainePhysics and Astronomy
ThématiqueGyrotron and Vacuum Electronics Research
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésDementiaNeuropsychologyCognitionCognitive declinePsychological interventionPopulationQuality of life (healthcare)Cognitive impairmentAnosognosia
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

The prevalence of neurodegenerative disorders, especially dementia, is increasing as the population ages (Hou et al., 2019; World Health Organisation, 2021). There are currently no cures for dementia, but early treatments and interventions may slow disease progression and improve quality of life (Liss et al., 2021; Livingston et al., 2020). Despite early declines in memory and executive functioning (Erkkinen et al., 2018), dementia continues to be poorly detected (Amjad et al., 2018; Lang et al., 2017; Walker et al., 2017). The challenges in detecting dementia early are examined in Chapter 1, including reports of cognitive decline being unforthcoming or inaccurate and clinicians having limited time to conduct cognitive assessments (Olivari et al., 2020; Pink et al., 2018). Consequently, cognitive screens are recommended to detect cognitive decline quickly and objectively (Ismail Z et al., 2020; Pink et al., 2018). Chapter 1 examines how cognitive screens can expedite the assessments that are required to diagnose dementia (Roebuck-Spencer et al., 2017), facilitate access to interventions (Pink et al., 2018), and help identify older adults who are at risk of experiencing difficulties with independent functioning, decision-making, mental health and wellbeing (Ahlqvist et al., 2016). Chapter 2 evaluates some of the most popular cognitive screens and recognises that they are less accurate for detecting cognitive decline than more time-intensive and comprehensive neuropsychological assessments (Summers et al., 2019). Neuropsychological assessments often examine executive functioning by administering tasks involving response inhibition, such as sorting tests (Wallace et al., 2022), which can detect dementia and MCI (Rabi et al., 2020). However, sorting tests are rarely used for screening purposes (Hobson, 2007). In reviewing the common cognitive screens, such as the Mini Mental Status Examination and Montreal Cognitive Assessment, Chapter 2 notes they do not include sorting tasks, and are limited by their administration and scoring time, user-friendliness, availability, reliability, and ability to detect cognitive decline (Hemmy et al., 2020; Larner, 2013). Although some sorting tests are quick to administer and provide a promising alternative to common cognitive screens, they often use materials that are not readily available and there is limited information regarding their reliability (Beglinger et al., 2008; Hobson, 2007). Moreover, data relating to their effectiveness for detecting cognitive decline in older adults who have a neurodegenerative disorder had yet to be synthesized. The longstanding use of sorting tests in research and psychological practice suggested a meta-analysis would be useful to determine their effectiveness for detecting cognitive decline in older adults. Study 1 (Chapter 3) involved a meta-analysis of 142 studies that used sorting tests in older adults (≥60 years of age) with and without a neurodegenerative disorder, including dementia and Parkinson’s disease. This study found sorting tests were highly effectively for differentiating between those with and without a neurodegenerative disorder, especially dementia. In addition, their effectiveness seems to rival the Mini Mental Status Examination (MMSE; Mitchell, 2009), suggesting they may provide a viable alternative to this popular screen. Incidentally, the meta-analysis found sorting tests did not reliably differentiate between behavioural-variant fronto-temporal dementia and Alzheimer’s dementia, which has significant clinical implications because they are often used for this purpose (American Psychiatric Association, 2013; Musa et al., 2020; Gustafson, et al., 1998; Possin et al., 2013). Of the different scores that sorting tests yield, the Category (grouping stimuli into categories) and Description (explaining the underlying categories) scores proved to be most effective for screening older adults for cognitive decline. Study 2 (Chapter 4) introduced a newly developed cognitive screen – the QuickSort, which was designed to improve upon existing sorting tests (e.g.,Weigl). The QuickSort uses nine stimuli that need to be sorted by colour, shape and number, with the person additionally being required to explain/describe the basis for their correct sorts. It was designed to be quicker than existing sorting tests because it uses less stimuli and provides an early discontinuation rule for intact performance. The QuickSort also captures different levels of cognitive impairment through the use of additional trials and prompts. Designed for a wide range of older adults, QuickSort scores can be computed even when an examinee finds it too difficult to complete or expressive language problems/low English proficiency prevent a person from explaining their sorts. The QuickSort stimuli, record form and manual are published online. Study 3 (Chapter 5) involved the development of an iPad-compatible version of the QuickSort, called the QuickSort-e. This version of the test was specifically designed to improve the ease with which the test could be administered and scored in a standardized manner, reduce scoring errors and training requirements, and remove the need for physical stimuli and record forms. The QuickSort-e can share patients’ records, which may assist in continuity of care, and store their information for clinical auditing (e.g., to determine patient characteristics) and research purposes. Study 4 (Chapter 6) investigated the user-friendliness, and inter-rater and test-retest reliabilities of the QuickSort. It was administered to older (≥60 years) community-dwelling adults (n = 187) and inpatients referred for neuropsychological assessment (n = 78). The QuickSort was completed in less than two minutes by a cognitively-healthy subgroup (n = 115, defined using MMSE and FAB scores), confirming its brevity. QuickSort scores <2 and ≥17 increased and reduced the likelihood that an older adult was impaired on the MMSE or FAB or both of these screens by a factor of 9.26 (95% CI: 2.96 – 28.75) and 0.16 (95% CI: 0.06 – 0.41), respectively. Furthermore, the accuracy with which the QuickSort detected cognitive impairment improved when the prevalence of impairment on the MMSE and FAB in the specific healthcare setting was additionally considered. Overall, Study 4 found that the QuickSort is quick, easy, reliable, and a valid cognitive screen for detecting cognitive impairment in older adults. Study 5 (Chapter 7) examined the QuickSort in relation to the complex clinical scenario of providing information regarding the lifestyle decision-making capacity of inpatients (LS-DMC; n = 124). In busy healthcare settings clinical interviews are used identify the inpatients needing comprehensive LS-DMC assessments in order to classify them as lacking or not-lacking LS-DMC. Of the information available at the interview stage, which included cognitive screening performances on the MMSE and FAB, the QuickSort best differentiated between those who lacked LS-DMC and those who did not. Low (<2) and high (≥13) QuickSort scores increased or reduced the likelihood that a person lacked LS-DMC by a factor of 65.26 (95% CI: 2.91 – 1463.90) and 0.32 (95%CI: 0.18 – 0.57), respectively. In healthcare settings where many (58%) inpatients lack LS-DMC, the probability of inpatients lacking LS-DMC increased to 99% when their QuickSort scores were <2 and reduced to 30% with scores ≥13. Thus, the QuickSort appears to provide a viable alternative to other cognitive screens that are used at the initial clinical interview stage to provide information regarding inpatients’ LS-DMC. Overall, the rising prevalence of neurodegenerative disorders and associated cognitive decline is increasing the demand for cognitive screens (Connor, 2021), but existing measures are limited by the time they take to administer, their reliability and the accuracy with which they detect cognitive decline (Larner, 2016). Sorting tests are rarely used for screening (Hobson, 2007), but can effectively detect cognitive decline in older adults. The QuickSort is a new sorting test that provides a brief, reliable, and effective alternative to lengthier screens that are used for detecting cognitive impairment in older adults and appears to provide useful preliminary information regarding their LS-DMC.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,005
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Autre · Signal consensuel: aucune
Score de désaccord entre enseignants0,010
Score d'incertitude au seuil0,033

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,005
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0020,001
Études des sciences et des technologies0,0010,000
Communication savante0,0010,001
Science ouverte0,0010,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0100,005

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 Opus0,031
Tête enseignante GPT0,352
Écart entre enseignants0,321 · 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 source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreAutre

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

En bref

Citations0
Publié2022
Routes d'admission1
Résumé présentoui

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