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Record W2116014213 · doi:10.1682/jrrd.2004.01.0008

Electromyographic activity imbalances between contralateral back muscles: An assessment of measurement properties

2005· article· en· W2116014213 on OpenAlexaff
Denis Gagnon, André Arsenault, Denis Gravel, Patrick Loisel

Bibliographic record

VenueThe Journal of Rehabilitation Research and Development · 2005
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsInstitut de recherche Robert-Sauvé en santé et en sécurité du travail
Fundersnot available
KeywordsIsometric exerciseLow back painPhysical medicine and rehabilitationElectromyographyMedicineTrunkMotor controlBack painBack musclesPhysical therapy

Abstract

fetched live from OpenAlex

Electromyographic (EMG) contralateral imbalances of back muscles are often interpreted as an aberrant back muscle pattern related to back pain. This study assessed different measurement properties (influence of the control of asymmetric efforts and of the force level, reliability, and sensitivity to low back status) of EMG imbalance parameters. Healthy controls (n = 34) and chronic low back pain subjects (n = 55) stood in a dynamometer measuring the principal (extension) and coupled (lateral bending, axial rotation) L5/S1 moments during isometric trunk extension efforts. The results showed that back pain subjects did not produce higher coupled moments than controls. Providing feedback of the axial rotation moment to correct asymmetric efforts during the task did not reduce EMG contralateral imbalances, except for some extreme cases. Normalized EMG imbalance parameters remain relatively constant between 40% and 80% of the maximal voluntary contraction. The reliability of EMG imbalance parameters was moderate, at best. Finally, neither low back status nor pain location had an effect on EMG contralateral imbalances. We conclude that the clinical relevance of EMG contralateral imbalances of back muscles remains to be established.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.439
Threshold uncertainty score0.323

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.000
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 science0.0000.000
Research integrity0.0000.000
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 head0.114
GPT teacher head0.392
Teacher spread0.278 · 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

Citations16
Published2005
Admission routes1
Has abstractyes

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