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Record W4403249157 · doi:10.1002/mdc3.14225

Examining Agreement in Psychotic Symptom Assessment: Insights from Parkinson's Disease Dementia Dyads

2024· article· en· W4403249157 on OpenAlexfundno aff
Blake C. Beehler, Michelle Hyczy de Siqueira Tosin, Glenn T. Stebbins, Christopher G. Goetz

Bibliographic record

VenueMovement Disorders Clinical Practice · 2024
Typearticle
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsnot available
FundersDystonia CoalitionNational Institutes of HealthNIH Clinical CenterRush UniversityInternational Parkinson and Movement Disorder SocietyUniversity of OxfordAdamas PharmaceuticalsCHDI FoundationCleveland Clinic FoundationCleveland ClinicPfizerParkinson's FoundationOttawa Hospital Research InstituteEli Lilly and CompanyNeurocrine BiosciencesU.S. Department of Defense
KeywordsPsychologyParkinson's diseaseDementiaPsychiatryPsychosisClinical psychologyDiseaseDementia with Lewy bodiesMedicine

Abstract

fetched live from OpenAlex

BACKGROUND: Psychosis and cognitive decline often co-occur in Parkinson's Disease (PD), which complicates assessment. OBJECTIVE: We measured agreement between patients with PD and dementia (PDD) and care partners (CPs) in their independent evaluation of PD-related psychotic symptoms. METHODS: We compared responses to a PD psychosis rating scale (SAPS-PD) in 21 dyads of patients with PDD and cognitively normal CPs. We assessed the concordance of responses using the intraclass correlation coefficient (ICC). Following the psychosis assessment, the clinician used all available information and adjudicated who provided the most reliable responses. RESULTS: Dyads demonstrated poor concordance in summary scores (ICC = 0.464). Six of the nine individual items had poor agreement. The clinician adjudicated the patient's response as the more reliable in 71.4% of cases. CONCLUSIONS: Although many psychotic symptoms are internal and not observable, in the context of PDD, both patient and CP inputs are valuable, but final adjudication favors patient responses.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.263
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.044
GPT teacher head0.380
Teacher spread0.336 · 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.

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

Citations1
Published2024
Admission routes1
Has abstractyes

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