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Record W2896337832 · doi:10.1159/000494091

Using Motor Imagery Training to Increase Quadriceps Strength: A Pilot Study

2018· article· en· W2896337832 on OpenAlex
Tyler M. Saumur, Stephen D. Perry

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEuropean Neurology · 2018
Typearticle
Languageen
FieldPsychology
TopicSport Psychology and Performance
Canadian institutionsWilfrid Laurier UniversityToronto Rehabilitation InstituteUniversity of Toronto
Fundersnot available
KeywordsMotor imageryPsychologyPhysical medicine and rehabilitationStrength trainingMotor learningPhysical therapyMedicineNeuroscience

Abstract

fetched live from OpenAlex

<b><i>Background:</i></b> Motor imagery training implements neural adaptation theory to improve muscle strength without physically performing muscle contractions. To date, motor imagery training research regarding the efficacy of improving torque of the quadriceps over a brief training period is limited. <b><i>Objective:</i></b> To determine the impact of a 3-week motor imagery training on peak torque during knee extension. <b><i>Method:</i></b> Ten young, healthy volunteers were randomly assigned to 1 of 3 groups over a 3-week period: strength training, motor imagery training and control. <b><i>Results:</i></b> Following training, an increase in peak torque was observed in all strength training participants (mean change of 38 ± 15%) and in 2 members of the motor imagery training group (45 ± 10%). <b><i>Conclusion:</i></b> Brief periods of motor imagery training may have the potential to improve quadriceps strength; however, more research is needed with larger populations to test this hypothesis.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.234
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.006

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 head0.144
GPT teacher head0.370
Teacher spread0.226 · 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