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Record W2022058773 · doi:10.1097/bcr.0b013e3181ac0003

Effect of Motor Imagery in the Rehabilitation of Burn Patients

2009· article· en· W2022058773 on OpenAlexaff
Aymeric Guillot, Florent Lebon, Michel Vernay, Jean-Pierre Girbon, Julien Doyon, Christian Collet

Bibliographic record

VenueJournal of Burn Care & Research · 2009
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsMedicineMotor imageryRehabilitationPhysical medicine and rehabilitationBurn centerBurn injuryPhysical therapyPoison controlEmergency medicineSurgery

Abstract

fetched live from OpenAlex

Although there is ample evidence that motor imagery (MI) improves motor performance after CNS injury, it is still unknown whether MI may enhance motor recovery after peripheral injury and most especially in the rehabilitation of burn patients. This study aimed to investigate the effects of a 2-week MI training program combined with conventional rehabilitation on the recovery of motor functions in handed burn patients. Fourteen patients admitted to the Medical Burn Center were requested to take part in the study and were randomly assigned to the imagery or the control group. Behavioral data related to the ability to perform each successive step of three manual motor sequences were collected at five intervals during the medical procedure. The results provided evidence that MI may facilitate motor recovery, and the belief in the effectiveness of MI was strong in all patients. MI may substantially contribute to improve the efficacy of conventional rehabilitation programs. Hence, this technique should be considered as a reliable alternative method to help burn patients to recover motor functions.

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)0.011
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.560
Threshold uncertainty score0.919

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.040
GPT teacher head0.426
Teacher spread0.386 · 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 teacher head, 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

Citations23
Published2009
Admission routes1
Has abstractyes

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