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Record W4415439008 · doi:10.1302/1358-992x.2025.10.149

BIOMECHANICAL OUTCOMES FOR ADLS POST-RTSA ARE AFFECTED BY SCAPULAR MORPHOLOGY VARIATION: A SIMULATION STUDY

2025· article· en· W4415439008 on OpenAlexaff
Pavlos Silvestros, David Saliken, George S. Athwal, James Giles

Bibliographic record

VenueOrthopaedic Proceedings · 2025
Typearticle
Languageen
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsAcromionBiomechanicsCoracoidScapulaShoulder jointPopulationArthroplastyShoulder ProsthesisRotator cuff

Abstract

fetched live from OpenAlex

Reverse Total Shoulder Arthroplasty (RTSA) has become a common method of restoring shoulder function in patients with arthropathy. Despite RTSA accounting for 30 to 62% of shoulder replacements the biomechanical implications and functional outcomes of these operations are still to be fully understood, in part because the effects of patient bone morphology remain unknown. It is impractical to examine the effect of anatomical variability on functional outcomes experimentally or clinically, however computational anatomy, modelling and simulation methods can provide biomechanical information to help identify causal relationships across a patient population. The aim of our study was to evaluate the effect of scapular morphology on RTSA biomechanics with musculoskeletal models and predictive simulations. We hypothesised that morphological changes would strongly affect joint loading, muscle moment arms and muscle coordination during functional motions. A previously developed statistical shape model generated scapular morphologies by independently varying six modes of variation (Table 1) with four levels for each mode and the population average. Modes were chosen that varied the glenoid fossa, acromion process, scapular spine and coracoid process as these are believed to most strongly affect glenohumeral biomechanics. Glenospheres of 39 mm diameter were orientated to correct for superior glenoid inclination and positioned with a 7 mm inferior overhang and 2 mm lateralisation from the inferior most point of the glenoid following guidelines provided by an expert orthopaedic surgeon. The newly defined glenohumeral joint geometries resulting from the digital surgery were used to redefine a previously validated musculoskeletal shoulder model. Predictive simulations (OpenSim 4.3) were completed that required the model to achieve a lateral and upward reaching tasks in 3D space by minimising muscular effort and time taken. Optimal solutions were computed for the lateral and upward reaching tasks across all morphologies. Morphological variation affected muscle length and moment arms across the range of motion that resulted in changes to moment generation and joint loading (Figure 1). Inferior translation of the glenoid relative to the acromion seen in Modes 4 & 5 resulted in greater resultant glenohumeral joint loads in early abduction (0–40°) whereas anterior and medial translations seen in Modes 5 & 6 reduced joint loading in later stages (40–80°) of the lateral motion but not the upward reaching motion. This study demonstrates that existing generic RTSA construct placement guidelines will not yield the same biomechanical outcomes across a population due to the wide range of anatomical variations that affect muscle force, moment generating properties and joint loads. This is the first musculoskeletal modeling study to systematically investigate the effects of morphological variation. Wider scale studies such as these using validated models and simulation methods are needed to understand the interactions between morphological variations and RTSA construct configurations. This study provides foundational evidence that morphological factors should be considered when conducting preoperative RTSA planning. 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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.896

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.018
GPT teacher head0.332
Teacher spread0.314 · 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 routes1
Has abstractyes

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