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Record W2766694162 · doi:10.1017/s1041610217002265

Parkinson's disease mild cognitive impairment classifications and neurobehavioral symptoms

2017· article· en· W2766694162 on OpenAlexafffund
Kirstie L. McDermott, Nancy Fisher, Sandra Bradford, Richard Camicioli

Bibliographic record

VenueInternational Psychogeriatrics · 2017
Typearticle
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsWomen and Children’s Health Research InstituteUniversity of Alberta
FundersCanadian Institutes of Health Research
KeywordsCognitive impairmentParkinson's diseaseDiseaseMedicineCognitionPsychologyPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: We apply recently recommended Parkinson's disease mild cognitive impairment (PD-MCI) classification criteria from the movement disorders society (MDS) to PD patients and controls and compare diagnoses to that of short global cognitive scales at baseline and over time. We also examine baseline prevalence of neuropsychiatric symptoms across different definitions of MCI. METHODS: 51 PD patients and 50 controls were classified as cognitively normal, MCI, or demented using MDS criteria (1.5 or 2.0 SD below normative values), Clinical Dementia Rating Scale (CDR), and the Dementia Rating Scale (DRS). All subject had parallel assessment with the Neuropsychiatric inventory (NPI). RESULTS: We confirmed that PD-MCI (a) is frequent, (b) increases the risk of PDD, and (c) affects multiple cognitive domains. We highlight the predictive variability of different criteria, suggesting the need for further refinement and standardization. When a common dementia outcome was used, the Level II MDS optimal testing battery with impairment defined as two SD below norms in 2+ tests performs the best. Neuropsychiatric symptoms were more common in PD across all baseline and longitudinal cognitive classifications. CONCLUSIONS: Our results advance previous findings on the utility of MDS PD-MCI criteria for PD patients and controls at baseline and over time. Additionally, we emphasize the possible utility of other cognitive scales and neuropsychiatric symptoms.

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 score0.030
Threshold uncertainty score0.694

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 head0.031
GPT teacher head0.340
Teacher spread0.308 · 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

Citations28
Published2017
Admission routes2
Has abstractyes

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