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Record W2773969018 · doi:10.1080/23335432.2017.1364667

Electromyographic investigation of anterior and posterior regions of supraspinatus: a novel approach based on anatomical insights

2017· article· en· W2773969018 on OpenAlexafffund
Soo Y. Kim, Jong Bum Ko, Clark R. Dickerson, David F. Collins

Bibliographic record

VenueInternational Biomechanics · 2017
Typearticle
Languageen
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsUniversity of WaterlooWomen and Children’s Health Research InstituteUniversity of AlbertaUniversity of Saskatchewan
FundersPhysiotherapy Foundation of Canada
KeywordsAnatomyMedicineOrthodontics

Abstract

fetched live from OpenAlex

Supraspinatus is composed of anterior and posterior regions that are distinct. To date, the relative electromyographic (EMG) activity of these regions during different tasks has not been investigated. This work, thus, evaluated activity of the anterior and posterior regions of supraspinatus during isometric actions in different postures. Data were analyzed from 11 healthy participants. Fine-wire electrodes were inserted into the anterior and posterior regions of supraspinatus. EMG activity was recorded during isometric abduction and external rotation exertions against 5% of body weight resistance. Three postures for abduction (30°, 60°, and 90° of humeral abduction, scapular plane) and two for external rotation (0° and 90° humeral abduction) were tested. Each participant’s data were normalized to the peak root mean square (RMS) values for the corresponding region. The RMS of the anterior region was divided by that of the posterior to calculate muscle activation ratios. Non-parametric statistics were used for analyses. The median ratio was lower during external rotation at 90° abduction compared to abduction at 30° (P = 0.003). These results suggest that the two regions of supraspinatus are functionally distinct during isometric tasks. The posterior region may play a more dominant role in postures with higher degrees of abduction and during external rotation exertions.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.076
Threshold uncertainty score0.374

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.030
GPT teacher head0.303
Teacher spread0.273 · 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

Citations20
Published2017
Admission routes2
Has abstractyes

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