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Record W4403222212 · doi:10.1098/rsos.231997

The influence of talus size and shape on <i>in vivo</i> talocrural hopping kinematics

2024· article· en· W4403222212 on OpenAlexafffund
Anja‐Verena Behling, Luke A. Kelly, Lauren Welte, Michael J. Rainbow

Bibliographic record

VenueRoyal Society Open Science · 2024
Typearticle
Languageen
FieldMedicine
TopicFoot and Ankle Surgery
Canadian institutionsUniversity of AlbertaQueen's University
FundersAustralian Research CouncilNatural Sciences and Engineering Research Council of CanadaInternational Society of Biomechanics
KeywordsKinematicsGeologyPhysicsGeometryComputer scienceMathematicsClassical mechanics

Abstract

fetched live from OpenAlex

talocrural function is poorly understood. Understanding these form-function relationships in a healthy cohort is essential for advancing patient-specific treatments aimed at restoring function. Nine participants (five females) hopped on one leg while biplanar videoradiography and ground reaction forces were simultaneously collected. Three-dimensional bone models were created from computed tomography scans. Helical axes of motion were calculated for the talus relative to the tibia (rotation axes), and a cylinder was fitted through the talar dome (morphological axis). Bland-Altman plots and spatial angles were used to examine the level of agreement between the rotation and morphological axes. A shape model of 36 (15 females) participants was established, and a cylinder fit was morphed through the range of ±3 standard deviations. The rotation and morphological axes largely agree regarding their orientation and location during hopping. The morphological axes were consistently oriented more anteriorly during landing than the rotation axes. Some shape components affect talar dome orientation and curvature independent of size. This suggests that besides bone size, the shape of the talar dome might influence the movement pattern during locomotion. Our findings may further inform talocrural joint arthroplasty design.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.685
Threshold uncertainty score0.300

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.015
GPT teacher head0.299
Teacher spread0.284 · 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

Citations3
Published2024
Admission routes2
Has abstractyes

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