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Record W4389265248 · doi:10.1002/brb3.3329

The German version of the tablet‐based UCSF Brain Health Assessment is sensitive to early symptoms of neurodegenerative disorders

2023· article· en· W4389265248 on OpenAlexaboutno aff
Gianina Toller, Lorena Stäger, Dilaxy Kumurasamy, Patrick Callahan, Florian Köhn, Thomas Münzer, Ursi Kunze, Andreas U. Monsch, Kate L Possin, Katherine P. Rankin, Ansgar Felbecker

Bibliographic record

VenueBrain and Behavior · 2023
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
Fundersnot available
KeywordsNeurocognitiveMontreal Cognitive AssessmentCognitionMedicinePsychologyCognitive impairmentGermanPsychiatry

Abstract

fetched live from OpenAlex

INTRODUCTION: Cognition often remains unassessed in primary care. To improve early diagnosis of neurocognitive disorder (NCD) in Switzerland, the tablet-based UCSF brain health assessment (BHA) and brain health survey (BHS) were validated. METHODS: The German BHA, BHS, and Montreal Cognitive Assessment (MoCA) were administered to 67 patients with mild/major NCD and 50 controls. BHA includes subtests of memory, executive, visuospatial, and language functioning, and informant-based BHS asks about behavior and motor functioning. RESULTS: The complete instrument (BHA + BHS) was most accurate at detecting mild NCD (AUC = 0.95) and NCD without amyloid pathology (AUC = 0.96), followed by the BHA. All measures were accurate (all AUCs > 0.95) at distinguishing major NCD and NCD with amyloid pathology (Alzheimer's disease [AD]) from controls. DISCUSSION: The German BHA and BHS are more sensitive to mild NCD and non-AD presentations than the MoCA and thus have a high potential to identify patients with NCD in primary care earlier than currently used screens.

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)0.000
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 score0.337
Threshold uncertainty score0.251

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.015
GPT teacher head0.360
Teacher spread0.345 · 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

Citations5
Published2023
Admission routes1
Has abstractyes

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