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Record W4253140661 · doi:10.1123/jab.2017-0263

Estimates of Achilles Tendon Moment Arm Length at Different Ankle Joint Angles: Effect of Passive Moment

2018· article· en· W4253140661 on OpenAlexafffund
Jared R. Fletcher, Brian R. MacIntosh

Bibliographic record

VenueJournal of Applied Biomechanics · 2018
Typearticle
Languageen
FieldMedicine
TopicTendon Structure and Treatment
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAnkleAchilles tendonMoment (physics)Joint (building)Plantar flexionOrthodonticsTendonMedicineMathematicsPhysical medicine and rehabilitationPhysicsAnatomyStructural engineeringEngineeringClassical mechanics

Abstract

fetched live from OpenAlex

The length of a muscle’s moment arm can be estimated non-invasively using ultrasound, and the tendon excursion method. The main assumption with the tendon excursion method, however, is that the force acting on the tendon during passive rotation is constant. However, passive force changes through the range of motion, and moment arm is underestimated. We attempted to account for passive force on the measurement of Achilles tendon moment arm using the tendon excursion method in 8 male and female runners. Tendon excursion was measured using ultrasound while the ankle was passively rotated at 0.17 rad•s -1 . Moment arm was calculated at 5° intervals as the ratio of tendon displacement to joint rotation from 70° to 115°. Passive moment (M P ) was measured by a dynamometer. The displacement attributable to M P was calculated by monitoring tendon displacement during a ramp isometric maximum contraction. M P was 5.7±2.1 Nm at 70° and decreased exponentially from 70°-90°. °. This resulted in M P -corrected moment arms that were significantly larger than uncorrected moment arms at joint angles where M P was present. Further, M P -corrected moment arms did not change with ankle angle, which was not the case for uncorrected moment arms.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.050
Threshold uncertainty score0.763

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.010
GPT teacher head0.252
Teacher spread0.242 · 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 designBench or experimental
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

Citations7
Published2018
Admission routes2
Has abstractyes

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