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Record W4408555095 · doi:10.1098/rsos.241828

Mitigating stress: exploring how our feet change shape with size

2025· article· en· W4408555095 on OpenAlexafffund
Paige Treherne, Erin C.S. Lee, Michael J. Rainbow, Luke A. Kelly

Bibliographic record

VenueRoyal Society Open Science · 2025
Typearticle
Languageen
FieldMathematics
TopicMorphological variations and asymmetry
Canadian institutionsQueen's University
FundersAustralian Research CouncilNatural Sciences and Engineering Research Council of Canada
KeywordsAllometryCalcaneusJoint (building)ScalingAnatomyStress (linguistics)OrthodonticsBiologyMathematicsMaterials scienceGeologyBiomedical engineeringMedicineGeometryEcologyStructural engineering

Abstract

fetched live from OpenAlex

If human skeletal shape increases proportionally with size (isometric scaling), we would expect exponential increases in joint contact stress as individuals become larger. However, if skeletal shape changes as a function of size (allometric scaling), this can mitigate increases in joint contact stress by changing the surface area (SA)-to-volume ratio. Here, we explored whether human foot bones scale with allometry and, if so, to identify the shape features that are associated with bone size. Computed tomography scans of the two largest foot bones (talus, calcaneus) were obtained from 36 healthy individuals. We implemented a scaling analysis for each joint articular surface and bone. We performed a Procrustes ANOVA to establish the shape features associated with bone size. In line with our hypothesis, articular surfaces on the calcaneus scaled with positive allometry relative to bone volume, whereas total bone SA scaled with negative allometry. This indicates that articular surfaces grew at a faster rate than the overall bone SA. Interestingly, the calcaneus appeared more cube-like with increasing size. This may be important for the mitigation of internal bone stresses with increasing skeletal size. Our findings suggest distinct but varied scaling strategies within the foot. This may reflect the requirement to maintain healthy joint contact and internal bone stresses with increasing size.

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)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.543
Threshold uncertainty score0.659

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.127
GPT teacher head0.342
Teacher spread0.215 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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