MétaCan
Menu
Back to cohort
Record W7132549688

Effect of Pectoralis Minor Length on Scapular Endurance and Core Endurance in Young Women

2025· article· en· W7132549688 on OpenAlexaboutno aff
Hasan Gerçek

Bibliographic record

VenueKTO Karatay University Institutional Archive · 2025
Typearticle
Languageen
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsIsometric exerciseCore (optical fiber)Pectoralis major muscleTrunkScapulaTorso
DOInot available

Abstract

fetched live from OpenAlex

To evaluate the effects of dominant and non-dominant pectoralis minor (PM) muscle length on scapular and core muscle endurance in the absence of scapular dyskinesia. A total of 93 women without scapular dyskinesia were included in the study. Demographic data were recorded. PM length was measured with a caliper. The lateral scapular slide test (LSST) was used to evaluate scapular dyskinesia. Scapular muscle endurance was evaluated using the scapular isometric compression test and core endurance using McGill’s torso muscular endurance test battery. Multiple linear regression analysis revealed that dominant pectoralis minor length and pectoralis minor index and non-dominant pectoralis minor length and pectoralis minor index can affect lateral scapular slide test (LSST) neutral, LSST hands on the iliac crest, trunk flexion and trunk extension. The longest muscle endurance time was found in the scapular test (46.32), followed by the core tests performed in extension (30.37sec), flexion (26.17sec), dominant side bridge position (10.06sec), non-dominant side bridge position (9.36sec). PM length had no effect on scapular and core muscle endurance measured in the side bridge position in the absence of scapular dyskinesia. In the trunk flexion and extension positions was directly related to the dominant and non-dominant PM muscle length. Keywords: pectoralis minor length, scapular muscle endurance, core muscle endurance, dominant arm

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 score0.246
Threshold uncertainty score0.512

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 head0.010
GPT teacher head0.262
Teacher spread0.252 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueKTO Karatay University Institutional ArchiveSame topicShoulder Injury and TreatmentFrench-language works237,207