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Record W60578265 · doi:10.26021/6689

The MoCA and ADL Items Separate Mild Cognitive Impairment and Dementia in Parkinson's Disease

2011· article· en· W60578265 on OpenAlexaboutno aff
Vineetha Uthamaputhiran

Bibliographic record

VenueUniversity of Canterbury Research Repository (University of Canterbury) · 2011
Typearticle
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMontreal Cognitive AssessmentDementiaParkinson's diseaseNeuropsychologyCognitionNormativePsychologyActivities of daily livingCognitive impairmentMedicineDiseasePsychiatryInternal medicine

Abstract

fetched live from OpenAlex

The aim of this study is to establish a brief screening tool to classify PD patients as PD with normal cognition (PD-N), PD patients with mild cognitive impairment (PD-MCI) and PD patients with dementia (PD-D). There has been emerging evidence that the MoCA (Montreal Cognitive Assessment) shows potential for the brief assessment of cognition to differentiate among PD patients. One possible solution to further improve the discrimination among PD-D, PD-MCI and PD-N groups is to examine Instrumental Activities of Daily Living (IADL) measures in conjunction with the MoCA. A convenience sample of 162 patients suffering from PD and 53 volunteer control subjects were examined in a movement disorders center. Extensive neuropsychological testing was done to classify the PD patients into PD-N, PD-MCI or PD-D. The 24 patients were diagnosed as PD-D based on the Movement Disorders Society Task Force criteria. For PD-MCI, two criteria were used: 1.5SD:2 in one-domain (1.5 SD below the norms on two measures in at least one of four cognitive domains) and 1.5SD:1 in two-domains (1.5 SD below normative data in at least one measure but in two domains) which made a diagnosis of 34 and 39 PD-MCI patients respectively. The remaining patients were classified as PD-N. For both the MCI criteria, the results suggest that 1) for discriminating PD-MCI from PD-N, the MoCA is a sufficiently suitable screening measure that is not improved by adding ADL measures, 2) for distinguishing PD-D from PD-MCI, the MoCA and the full ADL-IS questionnaire can be administered to a patient suffering from PD. When time is limited and depending on the possibility of answering the questions regarding the ADL-IS items, the MoCA along with the Muddled and Complex Medication ADL-IS items should be administered. When no scores are obtained for Muddled, then MoCA along with Complex Medication ADL-IS item is sufficient to discriminate PD-D from PD-MCI. However, if no scores are obtained for Complex Medication item, then an average of four ADL-IS items should be taken along with the MoCA. This attractive brief screening tool helps in detection of cognitive impairment in the elderly.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.823

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.034
GPT teacher head0.258
Teacher spread0.224 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2011
Admission routes1
Has abstractyes

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