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Record W3133098043 · doi:10.1123/jab.2020-0238

Exposure to Sustained Flexion Impacts Lumbar Extensor Spinae Muscle Fiber Orientation

2021· article· en· W3133098043 on OpenAlexaff
Brendan L. Pinto, Daniel Viggiani, Jack P. Callaghan

Bibliographic record

VenueJournal of Applied Biomechanics · 2021
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsLumbarMedicineLow back painOrientation (vector space)AnatomyPhysical medicine and rehabilitationLumbar spineThrowingGeometrySurgeryMathematicsPhysics

Abstract

fetched live from OpenAlex

The lumbar extensor spinae (LES) has an oblique orientation with respect to the compressive axis of the lumbar spine, allowing it to counteract anterior shear forces. This mechanical advantage is lost as spine flexion angle increases. The LES orientation can also alter over time as obliquity decreases with age and is associated with decreased strength and low back pain. However, it is unknown if LES orientation is impacted by recent exposures causing adaptations over shorter timescales. Hence, the effects of a 10-minute sustained spine flexion exposure on LES orientation, thickness, and activity were investigated. Three different submaximally flexed spine postures were observed before and after the exposure. At baseline, orientation (P < .001) and thickness (P = .004) decreased with increasingly flexed postures. After the exposure, obliquity further decreased at low (pairwise comparison P < .001) and moderately (pairwise comparison P = .008) flexed postures. Low back creep occurred, but LES thickness did not change, indicating that decreases in orientation were not solely due to changes in muscle length at a given posture. Activation did not change to counteract decreases in obliquity. These changes encompass a reduced ability to offset anterior shear forces, thus increasing the potential risk of anterior shear-related injury or pain after low back creep-generating exposures.

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 score0.130
Threshold uncertainty score0.494

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.276
Teacher spread0.267 · 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

Citations5
Published2021
Admission routes1
Has abstractyes

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