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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 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)0.009
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.108
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.009

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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreDataset

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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