MétaCan
Menu
Back to cohort
Record W2085846426 · doi:10.1080/10255840802020404

Relative efficiency of abdominal muscles in spine stability

2008· article· en· W2085846426 on OpenAlexafffund
Navid Arjmand, A. Shirazi‐Adl, Mohamad Parnianpour

Bibliographic record

VenueComputer Methods in Biomechanics & Biomedical Engineering · 2008
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSagittal planeKinematicsStability (learning theory)BiomechanicsOblique caseAbdominal musclesControl theory (sociology)MedicineAnatomyPhysical medicine and rehabilitationComputer sciencePhysics

Abstract

fetched live from OpenAlex

Using an iterative kinematics-driven nonlinear finite element model, relative efficiency of individual abdominal muscles in spinal stability in upright standing posture was investigated. Effect of load height on stability and muscle activities was also computed under different coactivity levels in abdominal muscles. The internal oblique was the most efficient muscle (compared with the external oblique and rectus abdominus) in providing stability while generating smaller spinal loads with lower fatigue rate of muscles. As the weight was held higher, stability deteriorated requiring additional flexor-extensor activities. The stabilising efficacy of abdominal muscles diminished at higher activities. The difference in critical loads in frontal and sagittal planes computed in the absence of abdominal coactivity disappeared under prescribed coactivities suggesting an optimal system in stability. The central nervous system may settle for a less stable spine in favour of lowering the risk of injury. Findings could help introduce stability criterion in optimisation models.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
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)0.0010.000

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 head0.027
GPT teacher head0.332
Teacher spread0.304 · 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 source (direct Gemma or distilled Codex), 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

Citations39
Published2008
Admission routes2
Has abstractyes

Explore more

Same venueComputer Methods in Biomechanics & Biomedical EngineeringSame topicMusculoskeletal pain and rehabilitationFrench-language works237,207