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Record W2041722328 · doi:10.1519/jsc.0b013e3181a06401

Ballistic Abdominal Exercises: Muscle Activation Patterns During Three Activities Along the Stability/Mobility Continuum

2009· article· en· W2041722328 on OpenAlexafffund
Stuart M. McGill, Amy Karpowicz, Chad MJ Fenwick

Bibliographic record

VenueThe Journal of Strength and Conditioning Research · 2009
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTorsoElectromyographyPhysical medicine and rehabilitationTrunkStiffeningBiomechanicsAnatomyMedicineStructural engineeringEngineeringBiology

Abstract

fetched live from OpenAlex

The purpose of this study was to document the muscle activity and spine motion during several tasks requiring rapid abdominal contraction. Eight healthy men from a university population were instrumented to obtain surface electromyography of selected trunk and hip muscles, together with video analysis to calculate joint moments and electromagnetic lumbar spine position sensor to track spine posture. Exercises included a punch, throw, and a ballistic torso-stiffening maneuver. This study found that no muscle turned on significantly before any other muscle during both the 1-in. punch and ballistic torso-stiffening maneuver. Conversely, there was a significant order or muscle onset during the baseball throw. Muscles reached peak activation significantly before any other muscle during the baseball throw and 1-in. punch, but there were no significant differences for the torso-stiffening maneuver. The exercises quantified in this study demonstrated how muscle contraction dynamics change to meet differing demands for stiffening, for force/moment production, and for rapid movements. Specifically, it seems that there is an order of contraction when movement is the goal but not when just spine stability is required. Thus, a different intensity of abdominal bracing is required to achieve the different objectives of sports tasks and exercises.

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)0.003
metaresearch head score (Gemma)0.001
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 score0.719
Threshold uncertainty score0.481

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.028
GPT teacher head0.327
Teacher spread0.298 · 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

Citations28
Published2009
Admission routes2
Has abstractyes

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