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Record W4317103389 · doi:10.1123/jab.2022-0047

Left–Right Differential Erector Spinae Muscles Activation in Prone and Quadruped Positions

2023· article· en· W4317103389 on OpenAlexaff
Nader Farahpour, Mahboube Alemzadeh, M Mohammadi, محمدرضا رضایی, Paul Allard

Bibliographic record

VenueJournal of Applied Biomechanics · 2023
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsPhysical medicine and rehabilitationDifferential (mechanical device)Erector spinae musclesLeft and rightElectromyographyAnatomyComputer scienceMedicinePhysicsEngineeringStructural engineering

Abstract

fetched live from OpenAlex

Left-right differential erector spinae (ES) muscle strengthening is required to correct ES muscle imbalances. The objective was to test the effect of 6 body positions on the differential activation of the ES muscles. In 14 able-bodied young women, using a surface electromyography system, the bilateral ES muscles activity at the third lumbar (ESL3) and the 10th (EST10) and 6th (EST6) thoracic vertebral levels was measured with the contralateral arm and leg lifted in the prone and quadruped conditions and with a single arm lifted in the quadruped position. Results showed that the activity of the ESL3 was symmetrical (P > .05) and significantly smaller than that of the thoracic ES muscles in all body positions (P < .01). The EST10 and EST6 were differentially activated in all tests (P < .001). Besides, the differential activation was higher in the contralateral-arm and -leg lift in the quadruped position than in the other positions. In conclusion, contralateral-arm and -leg lift and single-arm lift in the quadruped and prone positions are capable of differentially activating the ES muscles on one side more than the other side. Further studies are recommended to examine the effectiveness of these exercises on the correction of ES muscle imbalances in clinical populations.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.531
Threshold uncertainty score0.307

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 head0.010
GPT teacher head0.266
Teacher spread0.256 · 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 designBench or experimental
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
Published2023
Admission routes1
Has abstractyes

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