MétaCan
Menu
Back to cohort
Record W7114766785 · doi:10.1123/jab.2024-0325

Are There Cumulative Changes in Lumbar Spine Passive Stiffness Throughout a Week of Prolonged Seated Work?

2025· article· en· W7114766785 on OpenAlexaff

Bibliographic record

VenueJournal of Applied Biomechanics · 2025
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMorningEveningMorning stiffnessSittingLumbar spineRange of motionLumbar

Abstract

fetched live from OpenAlex

This study assessed daily and weekly changes in spine mechanical properties, specifically range of motion and passive stiffness, in those with and without sitting-induced low back pain to determine if time-dependent changes in mechanical properties were related to pain development in prolonged sitting. Over 1 week, 20 participants performed their seated office work and attended 5 laboratory sessions (Monday morning and evening, Tuesday morning, Friday evening, and the following Monday morning) to measure lumbar spine stiffness in passive flexion. Accelerometers measured seated lumbar flexion-extension each workday. In the morning and evening, participants provided low back pain ratings and performed maximum voluntary flexion. Statistical tests compared over time and between pain statuses (nonpain < 10 of 100 mm). There were increases in maximum flexion from morning to evening (2.0°; P = .003) and decreases in angular breakpoints on Monday evening and Tuesday morning (4.6% and 6.8% of passive flexion; P ≥ .056). Classification of seated spine flexion-extension into the transition and high stiffness zones of the passive curve (ie, moderate-high flexion) revealed that sitting strained the posterior passive tissues, likely contributing to the changes in range of motion and stiffness. Nevertheless, alterations in spine properties did not accumulate throughout the week and were not different by pain status.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.653
Threshold uncertainty score0.508

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.017
GPT teacher head0.298
Teacher spread0.281 · 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

Citations2
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Applied BiomechanicsSame topicMusculoskeletal pain and rehabilitationFrench-language works237,207