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Record W2312879595 · doi:10.1080/00140139.2016.1151553

Sagittal plane lumbar loading when navigating an obstacle and carrying a load

2016· article· en· W2312879595 on OpenAlexaff
Jeevaka B. Kiriella, Carolyn J. Perry, Kara M. Hawkins, Camille J. Shanahan, William H. Gage, Anne Moore

Bibliographic record

VenueErgonomics · 2016
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsYork University
Fundersnot available
KeywordsSagittal planeObstacleLumbarPhysical medicine and rehabilitationBiomechanicsStructural engineeringTrunkJoint (building)OrthodonticsMedicineAnatomyEngineering

Abstract

fetched live from OpenAlex

The current study quantified lumbar loading while carrying an anterior load mass and navigating an obstacle. Eight healthy male participants walked down a walkway and crossed an obstacle under three randomised LOAD conditions; empty-box (2 KG), five kilogram (5 KG) and ten kilogram (10 KG). Each walk was assessed at two events: left foot mid-stance (LMS) and right toe-crossing (TC) to characterise any changes from approach to crossing. Measures of interest included: trunk pitch, L4/L5 joint moment, compression, joint anterior-posterior shear and erector spinae activation. Findings demonstrate that obstacle crossing extended posture by 50, 41, 44%, respectively for each carried load magnitude. Further, these results indicate that shear rather than compressive loading may be an important consideration during crossing due to increase by 8, 9, 22% from LMS to TC for each load magnitude tested. These results provide insight into sagittal lumbar loading when navigating an obstacle while carrying a load. Practitioner Summary: The risk of carrying while navigating obstacles on the lumbar spine is not completely understood. The forces at the lumbar spine while simultaneously carrying and obstacle crossing were analysed. Data indicate that carrying and obstacle crossing influence lumbar shear loads, thereby moderately increasing the relative risk at lumbar spine.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.866
Threshold uncertainty score0.319

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 head0.013
GPT teacher head0.267
Teacher spread0.254 · 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
Published2016
Admission routes1
Has abstractyes

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