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Record W4411461322 · doi:10.1080/24725838.2025.2517026

Arm Differences in Muscle Activity Characteristics During a Bilateral Simulated Overhead Work in Right-Handed and Ambidextrous Individuals

2025· article· en· W4411461322 on OpenAlexafffund
Erika Renda, Julie N. Côté

Bibliographic record

VenueIISE Transactions on Occupational Ergonomics and Human Factors · 2025
Typearticle
Languageen
FieldPsychology
TopicErgonomics and Musculoskeletal Disorders
Canadian institutionsMcGill UniversityJewish Rehabilitation Hospital
FundersInstitut de Recherche Robert-Sauvé en Santé et en Sécurité du Travail
KeywordsPhysical medicine and rehabilitationWork (physics)Overhead (engineering)ElectromyographyWearable computerTask (project management)MedicineMuscle fatiguePhysical therapyPsychologyComputer scienceEngineering

Abstract

fetched live from OpenAlex

Occupational ApplicationsIn this study, healthy adults performed repetitive bilateral overhead shoulder flexion to fatigue-failure while we measured electromyography of shoulder muscles. We found that ambidextrous and right-handed individuals, especially females, exhibit asymmetrical muscle activation patterns during the task. To enhance worker health and safety, handedness and sex should be considered when implementing workplace changes. For instance, our results may imply that job rotation strategies that alternate use of hands would be easier to implement for males. Moreover, to minimize risk of injury, bilateral asymmetry in muscle activity could be monitored using wearable technology. In our experiment, the mean time to fatigue-failure was under 5 min, which was enough to elicit asymmetry of muscle activation. Therefore, we recommend frequent breaks after a few minutes of work when feasible, in order to avoid asymmetrical loadings during bilateral manual work, especially among females, who both have higher injury risk and more bilateral asymmetry.

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)0.000
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 score0.012
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.020
GPT teacher head0.289
Teacher spread0.269 · 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

Citations1
Published2025
Admission routes2
Has abstractyes

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