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Record W2145000895 · doi:10.5014/ajot.2013.007880

Multiple Errands Test–Revised (MET–R): A Performance-Based Measure of Executive Function in People With Mild Cerebrovascular Accident

2013· article· en· W2145000895 on OpenAlexaff
M. Tracy Morrison, Gordon Muir Giles, Jennifer D. Ryan, Carolyn Baum, Alexander W. Dromerick, Helene J. Polatajko, Dorothy Farrar Edwards

Bibliographic record

VenueAmerican Journal of Occupational Therapy · 2013
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsUniversity of Toronto
FundersNational Institute on AgingJames S. McDonnell Foundation
KeywordsPsychologyMeasure (data warehouse)Test (biology)Accident (philosophy)Executive functionsClinical psychologyPsychiatryCognitionComputer scienceData mining

Abstract

fetched live from OpenAlex

OBJECTIVE. This article describes a performance-based measure of executive function, the Multiple Errands Test-Revised (MET-R), and examines its ability to discriminate between people with mild cerebrovascular accident (mCVA) and control participants. METHOD. We compared the MET-R scores and measures of CVA outcome of 25 participants 6 mo post-mCVA and 21 matched control participants. RESULTS. Participants with mCVA showed no to minimal impairment on measures of executive function at hospital discharge but reported difficulty with community integration at 6 mo. The MET-R discriminated between participants with and without mCVA (p ≤ .002). CONCLUSION. The MET-R is a valid and reliable measure of executive functions appropriate for the evaluation of clients with mild executive function deficits who need occupational therapy to fully participate in community living.

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)0.001
metaresearch head score (Gemma)0.004
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 score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.022
GPT teacher head0.265
Teacher spread0.242 · 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

Citations61
Published2013
Admission routes1
Has abstractyes

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