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Record W3097028072 · doi:10.1017/s104161022000277x

424 - Using the Montreal cognitive assessment in a memory clinic setting for triaging after initial assessment

2020· article· en· W3097028072 on OpenAlexaboutno aff
Géraud Dautzenberg, Jeroen G. Lijmer, Aartjan T.F. Beekman

Bibliographic record

VenueInternational Psychogeriatrics · 2020
Typearticle
Languageen
FieldHealth Professions
TopicHealth, Medicine and Society
Canadian institutionsnot available
Fundersnot available
KeywordsMontreal Cognitive AssessmentDementiaTriageMemory clinicCohortMedicineCognitionCognitive impairmentCohort studyPsychiatryGerontologyPsychologyInternal medicineDisease

Abstract

fetched live from OpenAlex

Objectives: More and more referrals to memory clinics are expected, but diagnostic routes are already challenged. In order to be able to follow the advice to diagnose dementia more often and earlier in the process, but also to be able to handle the increasing numbers of referrals, a fast but reliable triage test is needed. According to the Cochrane review, “the MoCA can help identify people who need specialist assessment and treatment for dementia”. It has been validated in multiple institutions and languages. However, many of these studies are designed with a case-control design using healthy, community-based individuals as controls, which can lead to spectrum bias. Our cohort of referrals to a memory clinic with patients suspected of having cognitive disorders (mild dementia and MCI) after initial assessment in an old age psychiatric clinic, needs to be validated because different settings can give different results. Design: our reference standard consisted of a consensus-based diagnosis according to international criteria for detecting MCI and MD, and this was compared with patients suspected of MCI/MD - but excluded from cognitive disorders (NoCI)- from the same cohort. Results: The mean MoCA scores differ significantly between the groups: 24 in NoCI, 21 in MCI and 16.5 in MD. The AUC of MD against non-demented (MCI+NoCI) was 0.83 resulting in 90% sensitivity, 65% specificity, 50% PPV and 94% NPV at a best cut-off of <21 according the Younden index. For CI (MD+MCI) against NoCI the results were respectively 0.77AUC, 95%sens, 47%spec, 88%PPV, 68%NPV at a cut-off <26. On an individual basis, as a box plot of DSM IV diagnoses showed, the MoCA score has limitations and clinical aspects need to be taken into account: FTD, high education to the upside; MCI including psychiatric etiology to the downside. Conclusions: by using a cut-off score of <21, 90% of people with positive MoCA have CI, while 94% of people with negative MoCA (≥21) will not have dementia. The MoCA can significantly reduce referrals (50%) by excluding patients for further diagnostic work-up at a memory clinic, even if they are suspected of CI after initial assessment.

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)0.003
metaresearch head score (Gemma)0.001
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 score0.154
Threshold uncertainty score0.766

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
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 integrity0.0000.002
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 head0.117
GPT teacher head0.540
Teacher spread0.424 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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