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Record W4399009072 · doi:10.7910/dvn/t4vb64

The effect of graded motor imagery training on pain, functional performance, motor imagery skills, and kinesiophobia after total knee arthroplasty: randomized controlled trial

2023· dataset· en· W4399009072 on OpenAlexaboutno aff
Büşra Candiri, Burcu Talu, Emre Guner, Metehan Ozen

Bibliographic record

VenueHarvard Dataverse · 2023
Typedataset
Languageen
FieldHealth Professions
TopicSports and Physical Education Research
Canadian institutionsnot available
Fundersnot available
KeywordsMotor imageryPhysical therapyPhysical medicine and rehabilitationRandomized controlled trialPsychologyMedicineTotal knee arthroplastyMotor skillNeuroscienceSurgeryElectroencephalography

Abstract

fetched live from OpenAlex

The aim was to investigate the effect of graded motor imagery (GMI) added to rehabilitation on pain, functional performance, motor imagery ability, and kinesiophobia in individuals with total knee arthroplasty (TKA). Individuals scheduled for unilateral TKA were randomized to one of two groups: control (traditional rehabilitation, n = 9) and GMI (traditional rehabilitation + GMI, n = 9) groups. The primary outcome measures were the visual analogue scale and the Western Ontario and McMaster Universities steoarthritis Index (WOMAC). Secondary outcome measures were knee range of motion, muscle strength, the timed up and go test, mental chronometer, Movement Imagery Questionnaire-3, lateralization performance, Central Sensitization Inventory, Pain Catastrophizing Scale, and Tampa Kinesiophobia Scale. Evaluations were made before and 6 weeks after surgery.

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0160.001

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 head0.020
GPT teacher head0.324
Teacher spread0.303 · 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 designRandomized trial
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
Published2023
Admission routes1
Has abstractyes

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