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Record W4311998057 · doi:10.1002/alz.069371

The Digital Maze Test (dMaze) Reveals Subtle Decision‐Making Difficulties in MCI: Hurrying to Keep Up?

2022· article· en· W4311998057 on OpenAlexaff
Dana L. Penney, Randall Davis

Bibliographic record

VenueAlzheimer s & Dementia · 2022
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsArtificial Intelligence in Medicine (Canada)
Fundersnot available
KeywordsTrail Making TestNeuropsychologyCognitive impairmentAudiologyTest (biology)CognitionPsychologyMedicinePsychiatry

Abstract

fetched live from OpenAlex

Abstract Background Traditional Maze tests are time‐consuming and insensitive to early AD cognitive changes. We combine behavior capture using a digitizing pen with novel test design to produce person‐centric/relative metrics that disembed cognitive from motor speeds. This enables comparison of complex decision‐making behavior across difficulty levels within a single test. We compare traditional measures with our person‐centric/relative measures. Method Participants were healthy (HC = 22), Mild Cognitive Impairment (MCI = 37), Alzheimer’s (AD = 39), and Parkinson’s (PD = 43) volunteers (N = 141). Diagnosis was determined by consensus using standard of care data (neuropsychological/neurological evaluations, MRI and DAT scans). Participant groups differed in age: HC (52.27) younger than PD (64.07), MCI (75.05) and AD (75.38) oldest. Groups differed in the expected direction for DCTclock and MoCA scores. The dMaze test contains a path‐following condition without choice points, followed by a choice (CH) condition with identical solution paths/motor demands. CH contains simple and complex choices. Relative measures use the participant’s overall speed to normalize within‐test speed deviations. We examined: Total Time (TTime) to completion, Max motor speed (MaxSpd) and Median speed (MedSpd), Choice point Average Speed (AveSpd) and Relative Average Speed (rAveSpd). Result TTime in seconds differentiated (p = .001) AD (141.24) from HC (52.98) and PD (77.35), not MCI (103.16). MaxSpd was slowest (p = .004) for HC (59.82mm/sec) compared to all clinical groups (MCI = 76.71mm/sec, PD = 77.11, AD = 85.52). Groups did not differ for MedSpd or choice point AveSpd. Simple choice point rAveSpd differentiated only AD (p = .001) from HC, PD & MCI. Complex choice point rAveSpd differentiated HC from MCI (p = .05) and AD (p = .001). HC and PD did not differ, suggesting simple motor speed did not account for ink speed differences. Conclusion Fast completion time despite slow maximum speed and relative average speed across choice points suggests HC use a modulated approach balancing speed and task difficulty. MCI faster relative maximum speed (hurrying) with completion time similar to HC, suggests a less well‐modulated approach. Relative speed differences for more complex choices may be sensitive to preclinical cognitive change even when total completion time is not. The ability to precisely measure and parse person‐centric motor and cognitive test behaviors provides opportunity to improve preclinical detection, disease monitoring and treatment efficacy.

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)0.000
metaresearch head score (Gemma)0.002
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 score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.027
GPT teacher head0.320
Teacher spread0.293 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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