Development and validation of a muscle wrapping model applied to intact and reverse total shoulder arthroplasty shoulders
Bibliographic record
Abstract
Assessment and optimization of procedural outcomes, namely joint replacement, that rely heavily on muscle action necessitates a model capable of accurately and reliably predicting muscle paths in an automated setting. In this study, such a model was developed and validated for the anatomic shoulder and one implanted with reverse total shoulder arthroplasty (rTSA), as these scenarios present particularly complex ranges of motion and wrapping geometries. A finite element (FE) element model included a "string-of-pearls" representation of the four rotator cuff muscles and the three deltoid muscle bundles. Muscle bundles consisted of 15 rigid spheres connected by linearly elastic springs and attached to the bones at their origins. The free ends of the muscle bundles were pulled to their insertions, after which motions were applied to the shoulder. Muscle moment arms were calculated and compared to data available in the literature qualitatively and using Pearson rho values and root-mean-square errors. The process was repeated following implantation of an rTSA. The FE model captured muscle paths throughout 180° of motion in under seven minutes. Moment arms at 30° and 60° of scaption generally fell within the ranges predicted by previous experimental and computational studies. The FE model showed good qualitative agreement with previously published results for abduction, flexion, and axial rotation before and after rTSA. In conclusion, a model capable of predicting muscle paths in the presence of variable wrapping geometry was developed and validated without sacrificing enough computational efficiency to render its use impossible in numerical techniques such as design optimization. © 2018 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 36:3308-3317, 2018.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".