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Record W2753832537 · doi:10.1177/09544119211012115

Statistical shape modelling to analyse the talus in paediatric clubfoot

2021· article· en· W2753832537 on OpenAlexaff
Yixuan Feng, Aaron Bishop, D. Farley, Joseph Mitchell, Kenneth J. Noonan, Xiaoping Qian, Heidi‐Lynn Ploeg

Bibliographic record

VenueProceedings of the Institution of Mechanical Engineers Part H Journal of Engineering in Medicine · 2021
Typearticle
Languageen
FieldMedicine
TopicFoot and Ankle Surgery
Canadian institutionsQueen's University
Fundersnot available
KeywordsClubfootDeformityPonseti methodOrthodonticsMagnetic resonance imagingResidualMedicineStatistical analysisComputer scienceMathematicsRadiologyAlgorithmStatistics

Abstract

fetched live from OpenAlex

One fifth of idiopathic clubfoot deformities cannot be fully corrected by Serial Ponseti casting and deformity recurs in 20%-30% of cases. To avoid x-ray exposure, the joints with largely unossified bones are diagnosed with magnetic resonance images (MRI). Typically, geometric measurements are made in the MRI planes; however, this method is inaccurate compared to measurements on three-dimensional (3D) models of the joint. More accurate measurements using the 3D bone shapes may be better at identifying differences between groups; and therefore, improve diagnosis. The entire set of shape features from MRI can be analysed simultaneously through statistical shape modelling (SSM) which assesses bone morphology of clubfoot in a more sensitive way. A method for SSM of the talus is developed in this study and the shape of the normal talus is compared with the one in clubfeet with residual deformity through both geometric measurements and SSM. Significant differences between two groups were found by both methods; and therefore, might contribute to improve diagnosis of clubfoot.

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)0.002
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.491

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.024
GPT teacher head0.258
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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations10
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the Institution of Mechanical Engineers Part H Journal of Engineering in MedicineSame topicFoot and Ankle SurgeryFrench-language works237,207