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Record W4415438970 · doi:10.1302/1358-992x.2025.10.103

STATISTICAL SHAPE MODELLING TO COMPARE SPINOPELVIC STRUCTURES WITH HIP IMPINGEMENT AND DYSPLASIA

2025· article· en· W4415438970 on OpenAlexaff
Simon Dobransky, Alexander J Hoffer, Hadi El Daou, Ryan M. Degen, Gabriel Ng

Bibliographic record

VenueOrthopaedic Proceedings · 2025
Typearticle
Languageen
FieldMedicine
TopicHip disorders and treatments
Canadian institutionsWestern University
Fundersnot available
KeywordsCadaveric spasmAcetabulumHip dysplasiaCadaverSacrumDysplasiaFemoral headRadiography

Abstract

fetched live from OpenAlex

Femoroacetabular impingement (FAI) and developmental dysplasia of the hip (DDH) are associated with adverse hip loading leading to early osteoarthritis. FAI can be subclassified into cam-type (aspherical head-neck deformity) and pincer-type (acetabular over-coverage or retroversion) while DDH is characterized by a shallow acetabular undercoverage. Spinopelvic parameters and femoral neck angles can affect hip mobility and symptoms, however, additional features related to FAI and DDH have never been comprehensively studied. The purpose was to examine how anatomical variations could best predict differences between range of motion in cam FAI, retroversion FAI, and DDH populations. Fifty-two cadaveric hips (n = 52; m:f = 28:24, age = 44 ± 11 yrs, BMI = 25 ± 6 kg/m2; approved ethics #MEC-AA-13-032) were obtained from a tissue bank and computed tomography (CT) scanned (Somatom Perspective; SIEMENS). Hip specimens were sub-grouped if they indicated a cam morphology (CAM; 3:00 alpha angle > 50.5°, 1:30 alpha > 60°), acetabular retroversion (RETRO; crossover sign, posterior wall sign, retroversion index), dysplasia (DDH; lateral centre-edge angle 10°, femoro-epiphyseal acetabular roof index > 2°), or healthy control (CON; no morphology). Each specimen's 3D models were reconstructed from their CT data using segmentation software (3D Slicer, The Slicer Community); and imported into statistical shape modelling software (ShapeWorks; University of Utah). Resultant mean shape models of the femur, pelvis, and sacrum were generated using principal component analysis and each pathological group was compared directly to the control group to highlight specific anatomical differences. For range of motion, each cadaveric specimen mounted onto a 6-DOF robot (TX90, Stäubli) that captured internal-external rotations in five sagittal positions: full extension, neutral 0o, flexion 30o, flexion 60o, and flexion 90o. One-way ANOVA examined range of motion differences between groups (CI = 95%); and were compared to the statistical shape models to associated hip morphology with mobility. From the statistical shape models, CAM showed greater acetabular coverage and RETRO showed greater femoral neck width compared to CON, respectively (Figure 1). Interestingly, the CAM sacrum showed an increased inclination of the sacral promontory compared to CON (Figure 2). Furthermore, the CON sacral alar width was significantly greater compared to all other pathological groups. From the range of motion, the RETRO and DDH groups showed an increased range of motion in full extension and flexion positions compared to CON, respectively. No difference was observed between the FAI groups (CAM and RETRO) in any of the flexion positions. The most important finding was that the CAM group indicated a prominently inclined sacral promontory associated with non-restricted hip mobility. An increased sacral promontory may affect pelvic incidence and sagittal moment arms that would typically serve as a protective factor in the development of symptoms and complications. Furthermore, a shorter sacral alar width in the pathological groups (CAM, RETRO, DDH) may play a factor in decreased trunk stability and development of symptoms and complications. The characteristic femoral and acetabular morphologies alone may not be the primary indicators of early clinical signs, restrictive symptomatic hip impingement, or dysplastic instability. For any figures or tables, please contact the authors directly.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.660

Codex and Gemma teacher scores by category

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

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 head0.014
GPT teacher head0.275
Teacher spread0.261 · 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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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