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Record W2150440626 · doi:10.1123/jab.2013-0247

Head and Arm Positions that Elicit Maximal Voluntary Trunk Range-of-Motion Measures

2014· article· en· W2150440626 on OpenAlexfundno aff
Alison Schinkel-Ivy, Sara Pardisnia, Janessa D.M. Drake

Bibliographic record

VenueJournal of Applied Biomechanics · 2014
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsnot available
FundersYork University
KeywordsTrunkRange of motionHead (geology)AnatomyPosition (finance)Motion (physics)Physical medicine and rehabilitationGeologyPhysicsGeometryOrthodonticsMathematicsMedicinePhysical therapyBiology

Abstract

fetched live from OpenAlex

Relationships have been shown between spinal motion and head and arm postures, yet there has been little standardization of the head and arm positions that elicit maximal voluntary spine angles during maximal trunk flexion, lateral bend, and axial twist. This study aimed to determine the head and arm positions that facilitated maximum voluntary range of motion in various spinal regions during these movements. Twenty-four individuals performed maximal movements in each plane with different combinations of head and arm positions (flexion and lateral bend: four combinations; axial twist: six combinations). Generally, greater angles were elicited for the upper spine regions when the head was moved in the direction of trunk motion, while the angles of the lower regions were either unaffected or greater when the head was kept in a neutral position. Arm positions also affected maximum spinal angles, in that angles were greatest when the arms were hanging to the floor (flexion), abducted to 90° (axial twist), and either hanging to the floor or crossed over the chest (lateral bend). These findings provide insight into the interplay between the spine and adjacent segments and constitute an initial attempt to develop standardized positions during maximum range-of-motion 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)0.001
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 score0.500
Threshold uncertainty score0.353

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 head0.015
GPT teacher head0.263
Teacher spread0.248 · 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

Citations10
Published2014
Admission routes1
Has abstractyes

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