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Record W2415922642 · doi:10.2340/1650197799224175180

Measurement of torque of trunk flexors at different velocities

2020· article· en· W2415922642 on OpenAlexaff
J. Wessel, David Ford, David van Driesum

Bibliographic record

VenueJournal of Rehabilitation Medicine · 2020
Typearticle
Languageen
FieldEngineering
TopicMuscle activation and electromyography studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsIsometric exerciseTorqueConcentricTrunkIntraclass correlationEccentricPhysical medicine and rehabilitationPhysical therapyMedicineMathematicsPhysicsReproducibilityStatisticsGeometry

Abstract

fetched live from OpenAlex

The purpose of this study was to determine the test-retest reliability of recording isometric and isokinetic torque of the trunk flexors and to examine the effect of velocity on the torque curves. Thirty healthy subjects were tested on two occasions for isometric torque of trunk flexion at four angles and eccentric and concentric torque at three velocities. Two subjects repeated these tests in the passive mode to determine the torque produced by the trunk when there was no active flexion effort. Intraclass correlation coefficients were above 0.85 for all isometric and isokinetic measures. Standard errors of measurement ranged from 6.9 to 19.5 Nm. Student t-tests indicated no significant differences between occasions for all outcome measures. Examination of passive and active torque curves indicated that the torque produced by the mass of the trunk increased with increasing velocity. It is concluded that both isometric and isokinetic testing of the trunk flexors are reliable, but that testing at higher velocities may not provide a valid measure of muscle performance.

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)0.001
metaresearch head score (Gemma)0.006
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 score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.019
GPT teacher head0.227
Teacher spread0.208 · 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

Citations13
Published2020
Admission routes1
Has abstractyes

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