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Record W4409074909 · doi:10.1177/15459683251327568

Psychometric Properties of the Wolf Motor Function Test (WMFT) and Its Modified Versions: A Systematic Review With Meta-Analysis

2025· review· en· W4409074909 on OpenAlexaff
Lorena Sabrina Pometti, Daniele Piscitelli, Alessandro Ugolini, Francesco Ferrarello, Francesco Notturni, Andrea Coppari, Serena Caselli, Fabio La Porta, Mindy F. Levin, Leonardo Pellicciari

Bibliographic record

VenueNeurorehabilitation and neural repair · 2025
Typereview
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsMcGill University
Fundersnot available
KeywordsIntraclass correlationPsychologyChecklistConstruct validityCriterion validityClinical psychologyPhysical medicine and rehabilitationMeasurement invarianceReliability (semiconductor)Physical therapyPsychometricsMedicineCognitive psychologyConfirmatory factor analysisMachine learningStructural equation modelingComputer science

Abstract

fetched live from OpenAlex

Background The Wolf Motor Function Test (WMFT) and its modified versions are widely used to assess upper limb (UL) function in stroke survivors. However, comprehensive evaluations of its psychometric properties are lacking. Objective To perform a systematic review with meta-analysis on the psychometric properties (following the COnsensus-based Standards for the selection of health Measurement INstruments [COSMIN] taxonomy) of the WMFT and modified versions in stroke survivors. Methods Six databases were searched until May 2024 for studies examining at least one WMFT measurement property in stroke patients. Two independent reviewers conducted study selection, data extraction, and quality assessment using the COSMIN Risk of Bias checklist and quality of evidence (QoE) with the Grading of Recommendations Assessment, Development, and Evaluation approach. Meta-analyses synthesized psychometric properties reported in at least two studies. Results Twenty-five studies (N = 2044) were included. Regarding the WMFT Functional Ability Scale (FAS) and TIME scales, internal consistency (alpha ≥ .88), intra-rater (intraclass correlation coefficient [ICC] ≥ .97) and inter-rater (ICC ≥ .92) reliability, measurement error for TIME, construct validity (strong correlations [ r ≥| .64|] with Fugl-Meyer Assessment and Action Research Arm Test), and responsiveness (ES ≥ 0.48) were rated sufficiently with QoE from very low to high. Measurement error for FAS was assessed as inconsistent with moderate QoE, and cross-cultural validity was rated as indeterminate with very low QoE. Content validity was not assessed. Few studies investigated the psychometric properties of the modified versions. Conclusions WMFT demonstrates robust psychometric properties in assessing UL function in stroke survivors. While the WMFT-modified versions showed promising properties, further research is needed to use them. Future studies should focus on WMFT measurement error, content, and cross-cultural validity. Trial Review Registration: PROSPERO: CRD42021237425.

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)0.032
metaresearch head score (Gemma)0.089
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.032
Threshold uncertainty score0.168

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0320.089
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0210.039
Bibliometrics0.0100.011
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0030.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.081
GPT teacher head0.320
Teacher spread0.239 · 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 designMeta-analysis
Domainnot available
GenreReview

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

Citations7
Published2025
Admission routes1
Has abstractyes

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