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Record W2527167133 · doi:10.1123/jab.2015-0227

Influence of Load on Three-Dimensional Segmental Trunk Kinematics in One-Handed Lifting: A Pilot Study

2016· article· en· W2527167133 on OpenAlexfundno aff
Juliane Müller, Steffen Müller, Josefine Stoll, M. Rector, Heiner Baur, Frank Mayer

Bibliographic record

VenueJournal of Applied Biomechanics · 2016
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsnot available
FundersEuropean CommissionMcGill University
KeywordsTrunkKinematicsLumbarRange of motionWeight-bearingAnatomyPhysical medicine and rehabilitationRotation (mathematics)MedicineMathematicsOrthodonticsGeometryPhysical therapyPhysicsSurgeryBiology

Abstract

fetched live from OpenAlex

Stability of the trunk is relevant in determining trunk response to different loading in everyday tasks initiated by the limbs. Descriptions of the trunk's mechanical movement patterns in response to different loads while lifting objects are still under debate. Hence, the aim of this study was to analyze the influence of weight on 3-dimensional segmental motion of the trunk during 1-handed lifting. Ten asymptomatic subjects were included (29 ± 3 y; 1.79 ± 0.09 m; 75 ± 14 kg). Subjects lifted 3× a light and heavy load from the ground up onto a table. Three-dimensional segmental trunk motion was measured (12 markers; 3 segments: upper thoracic area [UTA], lower thoracic area [LTA], lumbar area [LA]). Outcomes were total motion amplitudes (ROM;[°]) for anterior flexion, lateral flexion, and rotation of each segment. The highest ROM was observed in the LTA segment (anterior flexion), and the smallest ROM in the UTA segment (lateral flexion). ROM differed for all planes between the 3 segments for both tasks (P < .001). There were no differences in ROM between light and heavy loads (P > .05). No interaction effects (load × segment) were observed, as ROM did not reveal differences between loading tasks. Regardless of weight, the 3 segments did reflect differences, supporting the relevance of multisegmental analysis.

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)0.001
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 score0.518
Threshold uncertainty score0.373

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.274
Teacher spread0.250 · 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

Citations3
Published2016
Admission routes1
Has abstractyes

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