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Comparison of the use of screening tools for evaluating cognitive impairment in patients with Parkinson's disease

2016· article· en· W2566493637 on OpenAlexaboutno aff
Carlos Henrique Ferreira Camargo, Eduardo de Souza Tolentino, A Bronzini, Marcelo Ladeira, Gustavo Leopold Schultz-Pereira, Marcelo Rezende Young Blood

Bibliographic record

VenueDementia & Neuropsychologia · 2016
Typearticle
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMontreal Cognitive AssessmentDementiaParkinson's diseaseTest (biology)Cognitive impairmentCognitionGold standard (test)MedicineDiseaseMini–Mental State ExaminationPsychologyInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Screening tests have been used for cognitive deficits in Parkinson's disease (PD). OBJECTIVE: This study compared the Montreal Cognitive Assessment (MoCA) test, the Mini-Mental State Examination (MMSE) and the clock drawing test for this purpose. METHODS: A total of 50 patients with PD were selected, 41 (82%) were diagnosed with dementia by the criteria of the Movement Disorder Society. The test Scales for Outcomes in Parkinson's Disease-Cognition (SCOPA-Cog) was used as the gold standard in comparison with the screening tests. RESULTS: The MoCA test (AUC=0.906) had a sensitivity of 87.80% and specificity of 88.89%. When the MMSE was associated with the clock drawing test (AUC=0.936), it had a specificity of 66.67% and sensitivity of up to 97.56%. CONCLUSION: The study suggests that the MoCA test can be a good screening test in PD. However, MMSE associated with the clock drawing test may be more effective than the MoCA test.

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 score0.024
Threshold uncertainty score0.392

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 studies0.0000.000
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 head0.104
GPT teacher head0.357
Teacher spread0.253 · 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

Citations13
Published2016
Admission routes1
Has abstractyes

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