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Record W1979640790 · doi:10.1080/00140130701813178

The effect of fatigue on trunk muscle activation patterns and spine postures during simulated firefighting tasks

2008· article· en· W1979640790 on OpenAlexafffund
Diane E. Gregory, Sonia Narula, Samuel J. Howarth, Caryl Russell, Jack P. Callaghan

Bibliographic record

VenueErgonomics · 2008
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Performance
Canadian institutionsUniversity of Waterloo
FundersCanada Research Chairs
KeywordsPhysical medicine and rehabilitationMuscle fatigueTrunkMedicineFlexibility (engineering)FirefightingPhysical therapyElectromyographyMathematics

Abstract

fetched live from OpenAlex

The purpose of this study was to determine the effect of a fatiguing task (3 min intense stair climbing) on the adopted spinal postures and trunk muscular activation patterns during three highly physically demanding simulated firefighting tasks. Following the fatigue protocol, it was observed that individuals adopted significantly greater spinal flexion (16.3 degrees maximum prior to fatigue as compared to 20.1 degrees post fatigue) and displayed reduced abdominal muscle activation as compared to before the fatigue protocol (mean ranging from 16.6% maximum voluntary contraction (MVC) to 30.6% MVC prior to fatigue as compared to ranging from 14.6% MVC to 25.2% MVC post fatigue). The reduced abdominal activation may be due to a reduction in co-contraction during these tasks, which may compromise spinal stability. Reduced co-contraction combined with the increased spinal flexion may increase the risk of sustaining an injury to the low back.

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)0.001
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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.037
GPT teacher head0.370
Teacher spread0.333 · 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

Citations34
Published2008
Admission routes2
Has abstractyes

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