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Record W2056426841 · doi:10.1080/00140139.2011.586062

Biering-Sorensen test performance of Japanese young males: comparison with other ethnicities and relationship to electromyography, near-infrared spectroscopy and exertion ratings

2011· article· en· W2056426841 on OpenAlexfundno aff
Jemma Coleman, Leon Straker, Amity Campbell, Hiroyuki Izumi, Anne Smith

Bibliographic record

VenueErgonomics · 2011
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsnot available
FundersMcGill University
KeywordsExertionEthnic groupElectromyographyTest (biology)PsychologyPhysical medicine and rehabilitationPhysical therapyMedicineSociologyBiology

Abstract

fetched live from OpenAlex

Back muscle endurance is a predictor of future low back pain and is commonly assessed using the Biering-Sorensen Test (BST). Differences exist between ethnic groups that may affect the performance and interpretation of the BST and should be investigated. This study's aim was to explore objective and subjective measures of the BST in a Japanese group in comparison with previous studies in other ethnic groups. A total of 27 young male Japanese students performed the BST while measures of muscle fatigue were collected. The mean BST time (152.7 (32.5) s) was greater than the median of the reported mean times in other ethnic groups over the previous decade (128.6 s). Objective measures indicated that the Japanese subjects' lumbar muscles were as fatigued as those of previous studies, while subjective measures appear to indicate that subjects under-reported exertion. The better performance of the Japanese subjects in the BST may reflect physical, psychosocial and lifestyle differences related to ethnicity. STATEMENT OF RELEVANCE: Ergonomics research and practice needs to be applicable to different ethnic groups. Despite the substantial body of evidence on back muscle endurance and indications of potential ethnicity related differences, this had not been previously investigated. These results help ergonomists to interpret physical ergonomics evidence in a multi-ethnic world.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.458

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.027
GPT teacher head0.249
Teacher spread0.222 · 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

Citations9
Published2011
Admission routes1
Has abstractyes

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