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Record W1960781338 · doi:10.1177/107110070002100309

Movement Coupling at the Ankle During the Stance Phase of Running

2000· article· en· W1960781338 on OpenAlexaff
Alex Stacoff, Benno M. Nigg, C. Reinschmidt, Antonie J. van den Bogert, Arne Lundberg, E. Stüssi, J. Denoth

Bibliographic record

VenueFoot & Ankle International · 2000
Typearticle
Languageen
FieldEngineering
TopicLower Extremity Biomechanics and Pathologies
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsCalcaneusAnkleMedicineCoupling (piping)TibiaHeelMovement (music)Physical medicine and rehabilitationAnatomySurgeryPhysicsMaterials scienceAcoustics

Abstract

fetched live from OpenAlex

The purpose of this study was to quantify movement coupling at the ankle during the stance phase of running using bone-mounted markers. Intracortical bone pins with reflective marker triads were inserted under standard local anaesthesia into the calcaneus and the tibia of five healthy male subjects. The three-dimensional rotations were determined using a joint coordinate system approach. Movement coupling was observed in all test subjects and occurred in phases with considerable individual differences. Between the shoe and the calcaneus coupling increased after midstance which suggested that the test shoes provided more coupling for inversion than for eversion. Movement coupling between calcaneus and tibia was higher in the first phase (from heel strike to midstance) compared with the second phase (from midstance to take-off). This finding is in contrast to previous in-vitro studies but may be explained by the higher vertical loads of the present in-vivo study. Thus, movement coupling measured at the bone level changed throughout the stance phase of running and was found to be far more complex than a simple mitered joint or universal joint model.

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 score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.014
GPT teacher head0.248
Teacher spread0.234 · 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

Citations53
Published2000
Admission routes1
Has abstractyes

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