Codex (Cognitive Disorders Examination) Decision Tree Modified for the Detection of Dementia and MCI

Ziso, Besa; Larner, A. J.

doi:10.3390/diagnostics9020058

Cited by 21 publications

(12 citation statements)

References 29 publications

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“…For the secondary study aim of MCI diagnosis, both tests were poor at differentiating MCI from SMC, an unsurprising finding since neither test was designed for this purpose, and as previously noted for Codex in this dataset 6,7 and in independent studies of Codex. 4,5,16,17 Mini-Cog appeared better than Codex for MCI diagnosis.…”

Section: Discussionsupporting

confidence: 57%

“…A previous attempt to circumvent the lack of sensitivity of Codex for cognitive impairment short of dementia by means of simple modifications of the decision tree proved unsuccessful. 7 The application of a simple logical "And" rule in the first Codex step (also known as conjunctive combination, or "believe the negative") might be anticipated to reduce sensitivity, NPV, and LR-, as observed with other applications of the "And rule". Application of the "Or" rule (compensatory combination; "believe the positive") may be advantageous for case finding as this approach generally improves sensitivity.…”

Section: Discussionmentioning

confidence: 99%

“…Data from a consecutive patient cohort referred to a dedicated cognitive function clinic based in a secondary care setting (neurology clinic) over a fixed time period of nine months (February-November 2012 inclusive) 6,7 and who were administered MMSE and CDT were re-analysed. Criterion diagnoses were dementia, mild cognitive impairment (MCI), or subjective memory complaint (SMC) by judgment of an experienced clinician, the former two diagnoses based on standard diagnostic criteria for dementia (DSM-IV; study data collection preceded publication of DSM-5) and MCI.…”

Section: Methods Participantsmentioning

confidence: 99%

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Mini-Cog versus Codex (cognitive disorders examination) Is there a difference?

Larner

2020

Dement. neuropsychol.

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Abstract. Mini-Cog and Codex (cognitive disorders examination) are brief cognitive screening tests incorporating word-recall and clock drawing tests. Objective: To assess and compare the screening accuracy of Mini-Cog and Codex for diagnosis of dementia and mild cognitive impairment (MCI) in patients attending a dedicated cognitive disorders clinic. Methods: Tests were administered to a consecutive cohort of 162 patients, whose reference standard diagnoses based on clinical diagnostic criteria were dementia (44), MCI (26), and subjective memory complaint (92). Results: Both Mini-Cog and Codex had high sensitivity (>0.8) for dementia diagnosis, but Codex was more specific. For diagnosis of MCI, Mini-Cog had better sensitivity than Codex. Weighted comparisons of Mini-Cog and Codex showed only marginal net benefit for Mini-Cog for dementia diagnosis but larger net benefit for MCI diagnosis. Conclusion: In this pragmatic study both Mini-Cog and Codex were accurate brief screening tests for dementia but Mini-Cog was better for identification of MCI.

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Section: Discussionsupporting

confidence: 57%

Section: Discussionmentioning

confidence: 99%

Section: Methods Participantsmentioning

confidence: 99%

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Mini-Cog versus Codex (cognitive disorders examination) Is there a difference?

Larner

2020

Dement. neuropsychol.

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“…Other examples include the Cambridge Assessment of Memory and Cognition, the Mini-Mental State Examination (MMSE) (it can assess impairments related to five cognitive functions such as orientation, attention, memory, language and visual-spatial skills by just asking subjects 11 questions [17]) test (its sensitivity is around 0.79, while its specificity is around 0.95) [26], the Saint Louis University Mental Status (SLUMS) (i.e., it encompasses a 30-point screening questionnaire to examine not only executive function but also orientation, memory, and attention) [27] and Cognitive Disorders Examination (Codex) (an ML-based assessment tool that combines a decision tree with the MMSE test and the clock drawing test to diagnose dementia. Codex has shown high sensitivity and specificity in detecting dementia) [28].…”

Section: Cognitive and Neuropsychological Assessmentsmentioning

confidence: 99%

Machine Learning to Assess Language Impairments in Older Adults

Parsa¹,

Alam²,

Mihailidis³

2020

Preprint

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Objectives: The main objective of this paper is to propose a methodology based on machine learning classifiers for assessing language impairments associated with dementia in older adults. To do so, we compare the impact of different types of language tasks, features, and recording media on our ML-based methodology’s efficiency. Methodology: The methodology encompasses the following steps: 1) Extracting linguistic and acoustic features from subjects’ speeches which have been collected from subjects with dementia ( N =9) and subjects without dementia ( N =13); 2) Employing feature selection methods to rank informative features; 3) Training ML classifiers using extracted features to recognize subjects with dementia from subjects without dementia; 4) Evaluating the classifiers; 5) Selecting the most accurate classifiers to develop the languages assessment tools. Results: Our results indicate that 1) we can find more predictive linguistic markers to distinguish language impairment associated with dementia from participants’ speech produced during the picture description language task than the story recall task. 2) a phone-based recording interface provides a more high-quality language dataset than the web-based recording systems; 3) classifiers trained with selected features from acoustic features or linguistic features show higher performance than the classifiers trained with pure features. Conclusion: Our results show that the tree-based classifiers that have been trained using the PD dataset can be used to develop an ML-based language assessment tool that can detect language impairment associated with dementia as quickly as possible.

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“…Ronan O'Caoimh and William Molloy compare the standardised Mini-Mental State Examination (sMMSE) to the Quick Mild Cognitive Impairment (Qmci) screen, finding the latter to have similar or greater accuracy in distinguishing all dementia subtypes and particularly MCI [8]. I present data from a large pragmatic study of the Mini-Addenbrooke's Cognitive Examination (MACE) [9,10], and with Besa Ziso describe attempts to modify the Cognitive Disorders Examination (Codex) [11] to improve detection of MCI [12].…”

mentioning

confidence: 99%

Diagnosis of Dementia and Cognitive Impairment

Larner

2019

Diagnostics

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In this special issue of Diagnostics, expert contributors have produced up-to-date research studies and reviews on various topics related to the diagnosis of dementia and cognitive impairment. The methods of the assessments discussed extend from simple neurological signs, which may be elicited in the clinical encounter, through cognitive screening instruments, to sophisticated analyses of neuroimaging and cerebrospinal fluid biomarkers of disease. It is hoped that these various methods may facilitate earlier diagnosis of dementia and its subtypes, and provide differential diagnosis of depression and functional cognitive disorders, as a prelude to meaningful interventions.

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Codex (Cognitive Disorders Examination) Decision Tree Modified for the Detection of Dementia and MCI

Cited by 21 publications

References 29 publications

Mini-Cog versus Codex (cognitive disorders examination) Is there a difference?

Mini-Cog versus Codex (cognitive disorders examination) Is there a difference?

Machine Learning to Assess Language Impairments in Older Adults

Diagnosis of Dementia and Cognitive Impairment

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