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Shoulder loading reliability in seated able-bodied subjects

2018· article· en· W2895001110 on OpenAlexaff
Richard Ballas, Nicolas Peyrot, Paul Allard, Teddy Caderby, Pascal Édouard, Georges Dalleau

Bibliographic record

VenueMedical Engineering & Physics · 2018
Typearticle
Languageen
FieldEngineering
TopicMuscle activation and electromyography studies
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsRepeatabilityBalance (ability)Reliability (semiconductor)Physical medicine and rehabilitationPhysical therapyCenter of pressure (fluid mechanics)RehabilitationMathematicsWork (physics)OrthodonticsMedicineStatisticsPhysics

Abstract

fetched live from OpenAlex

Shoulder performance and sensorimotor control assessments help to identify shoulder instabilities and document the rehabilitation progress. Testing seated subjects in a position of hand prehension requires less controlled adjustments to maintain body balance in a clinically relevant situation. The objective of this work was to determine the test-retest repeatability of a novel shoulder stability test in seated subjects with the ipsi-lateral hand in prehension during four arm loading conditions. Able-bodied subjects were seated on a rigid chair fixed to a force plate. A horizontally and posteriorly directed force was applied to the hand for four 4 loading conditions ranging from 0 to 3 kg. Ten postural balance parameters were calculated from the center of pressure displacements and its corresponding free moments. Intra-class correlation coefficients were calculated for three consecutive trials and for four loading conditions. Generally, the intra-class correlations values increased gradually with the load and varied from 0.727 to 0.948. Tz values increased non-linearly with the applied load. The test-retest reliability of a new shoulder stability test in seated able-bodied subjects was high with sufficient loading (3 kg) and 3 trials.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.540
Threshold uncertainty score0.827

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.009
GPT teacher head0.224
Teacher spread0.215 · 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 designSimulation or modeling
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
Published2018
Admission routes1
Has abstractyes

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