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Record W4387571034 · doi:10.1123/jab.2023-0085

Do Sex and Age Influence Scapular and Thoracohumeral Kinematics During a Functional Task Protocol?

2023· article· en· W4387571034 on OpenAlexaff
Alexander Waslen, Kenzie B. Friesen, Angelica E. Lang

Bibliographic record

VenueJournal of Applied Biomechanics · 2023
Typearticle
Languageen
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsKinematicsBiomechanicsMedicinePhysical medicine and rehabilitationScapulaRange of motionPhysical therapySurgeryAnatomy

Abstract

fetched live from OpenAlex

There is mixed evidence on the role that biological sex plays in shoulder biomechanics despite known differences in musculoskeletal disorder prevalence between males and females. Additionally, advancing age may contribute to shoulder kinematic changes. The purpose of this study was to determine if sex and age influenced scapular and thoracohumeral kinematics during a range of functional tasks. Sixty healthy participants aged 19-63 years (30 males; 30 females) completed a functional task protocol while their upper limb motion was recorded. Scapular and humeral angles were calculated and compared with multiple linear regressions to assess the interaction effects of sex and age. Shoulder kinematics were not different between sex and age groups for many of the functional tasks. However, females had lower humeral external rotation in the overhead lift task (15°, P < .001), and less scapular anterior tilt angles in the forward transfer task (6°, P < .001) than males. Age was positively associated with humeral elevation (R2 = .330, P < .001) and scapular rotation (R2 = .299, P < .001) in the Wash Axilla task. There exist some kinematic differences between sex and with advancing age for select functional tasks, which should be considered for musculoskeletal disorder development.

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 score0.204
Threshold uncertainty score0.432

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 head0.024
GPT teacher head0.308
Teacher spread0.285 · 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

Citations8
Published2023
Admission routes1
Has abstractyes

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