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Record W2030513035 · doi:10.1177/0891988712445086

Clinical Validity of the Mattis Dementia Rating Scale-2 in Parkinson Disease With MCI and Dementia

2012· article· en· W2030513035 on OpenAlexafffund
Evelyne Matteau, Nicolas Dupré, Mélanie Langlois, Pierre Provencher, Martine Simard

Bibliographic record

VenueJournal of Geriatric Psychiatry and Neurology · 2012
Typearticle
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsHôpital de l'Enfant-JésusUniversité Laval
FundersCanadian Institutes of Health Research
KeywordsDementiaRating scalePsychologyParkinson's diseasePsychiatryMedicineDiseasePhysical medicine and rehabilitationDevelopmental psychologyPathology

Abstract

fetched live from OpenAlex

The utility of the Mattis Dementia Rating Scale 2 (MDRS-2) in screening for dementia in Parkinson disease (PD) is well documented. However, little is known about its sensitivity to mild cognitive impairment in PD (PD-MCI). This study sought to document the validity of the MDRS-2 for diagnoses of PD-MCI and dementia in PD (PDD). Twenty-two healthy controls (HCs), 22 PD-MCI, and 16 PDD were compared on each MDRS-2 subscales and MDRS-2 total standard scores. Patients with PDD performed significantly worse than the other groups (all Ps < .05) on the MDRS-2 total and on all subscales, except attention. PD-MCI had significant lower scores than HCs on the MDRS-2 total and on initiation/perseveration and memory subscales. The optimal cutoff score for PD-MCI diagnosis was ≤ 140/144 and ≤ 132/144 for PDD. These findings suggest that MDRS-2 is a useful tool to identify dementia but that there might be a ceiling effect in the MDRS-2 cutoff score to diagnose MCI in PD.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.021
GPT teacher head0.288
Teacher spread0.267 · 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 source (direct Gemma or distilled Codex), 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

Citations60
Published2012
Admission routes2
Has abstractyes

Explore more

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