MétaCan
Menu
Back to cohort
Record W2965476997 · doi:10.1111/imj.14392

Comparing the Montreal Cognitive Assessment and Rowland Universal Dementia Assessment Scale in a multicultural rehabilitation setting

2019· article· en· W2965476997 on OpenAlexaboutno aff
Andrew Emerson, Poorani Muruganantham, Min Y. Park, Deren Pillay, Nikhil Vasan, Seong J. Park, Kim Linh Van, Preethi Pampapathi, Cui S. Seow, Siu‐Ming Yau

Bibliographic record

VenueInternal Medicine Journal · 2019
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
Fundersnot available
KeywordsMontreal Cognitive AssessmentMedicineFunctional Independence MeasureDementiaRehabilitationGerontologyCognitionCognitive impairmentPhysical therapyPsychiatryInternal medicineDisease

Abstract

fetched live from OpenAlex

In Australia it is recommended that all older people undergoing rehabilitation have a cognitive screen. We performed a longitudinal study comparing the correlation of two cognitive screening tools - the Rowland Universal Dementia Assessment Scale (RUDAS) and Montreal Cognitive Assessment (MoCA) with discharge outcomes in a geriatric inpatient setting. The RUDAS cut-off (<23/30) was associated with discharge to a nursing home (sensitivity 52%, specificity 70%). This was also noted with a MoCA cut-off <18/30 (sensitivity 57%, specificity 69%). Furthermore the association between the RUDAS and discharge destination was independent of its association with the Functional Independence Measure (r = 0.116; P = 0.275) and had a shorter administration time. Both RUDAS and MoCA scores could be used as predictors of discharge destination in a multicultural population.

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)0.004
metaresearch head score (Gemma)0.014
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 score0.028
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.013
GPT teacher head0.354
Teacher spread0.341 · 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

Citations5
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueInternal Medicine JournalSame topicDementia and Cognitive Impairment ResearchFrench-language works237,207