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Record W4407347217 · doi:10.1002/ajpa.70004

A New Method for Whole Bone Analysis of Bilateral Asymmetry

2025· article· en· W4407347217 on OpenAlexafffund
Valérie Deschênes, Michelle S.M. Drapeau

Bibliographic record

VenueAmerican Journal of Biological Anthropology · 2025
Typearticle
Languageen
FieldMathematics
TopicMorphological variations and asymmetry
Canadian institutionsUniversité de Montréal
FundersUniversité de Montréal
KeywordsAsymmetryComputer scienceAnatomyOrthodonticsMedicinePhysics

Abstract

fetched live from OpenAlex

Postcranial asymmetry of bones has been mostly studied with linear or angular measurements. Although conclusive, these measurements fail to capture the differences of the entire bone surface. Here, we develop a methodology to measure whole bone bilateral asymmetry from 3D models. We demonstrate the method using the humerus and the second metacarpal. We compare right and left bones of the same individual (bilateral variation) to that of different scans of the same bone (interscan variation) and of the same bone from different individuals (interindividual variation) to show that the method functions and is able to segregate different degrees of variation. The interscan variation is the lowest, while the interindividual variation is the greatest, and the bilateral variation falls between the other two. Visual comparisons, using color maps, illustrate on the bone where the asymmetry is most marked. As expected, the interscan comparisons show very little variation in shape, while the interindividual comparisons reveal extensive variation. In bilateral comparisons, some patterns were observed. In the humerus, the radial groove, the deltoid tuberosity, and the olecranon fossa were usually the most asymmetrical regions. The epiphyses are also more asymmetrical than the diaphysis. For the MC2, the attachments for the palmar interossei muscles and the articular facets with the MC3 were the most asymmetrical regions. These results demonstrate that this new method helps identify areas of asymmetry that would otherwise be difficult to observe.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.418
Threshold uncertainty score0.609

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.039
GPT teacher head0.402
Teacher spread0.363 · 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 designTheoretical or conceptual
Domainnot available
GenreMethods

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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