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Record W2804702108 · doi:10.5014/ajot.2018.027052

Predictive Value of the Cognitive Performance Test (CPT) for Staging Function and Fitness to Drive in People With Neurocognitive Disorders

2018· article· en· W2804702108 on OpenAlexaboutno aff
Theressa Burns, Katie Lawler, David Lawler, J. Riley McCarten, Michael A. Kuskowski

Bibliographic record

VenueAmerican Journal of Occupational Therapy · 2018
Typearticle
Languageen
FieldHealth Professions
TopicOlder Adults Driving Studies
Canadian institutionsnot available
Fundersnot available
KeywordsNeurocognitiveCognitionTest (biology)PsychologyValue (mathematics)MedicinePhysical medicine and rehabilitationClinical psychologyCognitive psychologyPsychiatryComputer scienceMachine learning

Abstract

fetched live from OpenAlex

The Cognitive Performance Test (CPT) is a standardized occupational therapy assessment that examines cognitive integration with functioning in an instrumental activities of daily living context. Conventional cognitive measures provide diagnostic utility but do not fully address the functional implications. Ninety-one veterans diagnosed with cognitive impairment were evaluated. We compared the predictive value of the CPT with the Large Allen Cognitive Level Screen (LACLS), Mini-Mental State Examination (MMSE), and Montreal Cognitive Assessment (MoCA) for the need to retire from driving versus ability to pass an on-road exam. Measures were also analyzed by diagnostic classification. CPT correctly classified a mild versus major neurocognitive disorder, whereas MMSE, MoCA, and LACLS did not differentiate the groups. A CPT cutoff score of <4.7/5.6 showed 89% sensitivity for failing the road exam and 75% specificity for ability to pass. CPT discriminated functional level in neurocognitive disorders and had better predictive value for fitness to drive compared with conventional cognitive measures.

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)0.000
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 score0.009
Threshold uncertainty score0.309

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.030
GPT teacher head0.377
Teacher spread0.347 · 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

Citations9
Published2018
Admission routes1
Has abstractyes

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