Choice of glenoid inclination correction method affects reverse shoulder arthroplasty baseplate loading
Bibliographic record
Abstract
Background When performing total shoulder arthroplasty, referencing the entire glenoid en face orientation to determine an inclination correction, termed here the “Total Shoulder Correction Angle” (TSCA), has proven helpful in positioning the anatomic glenoid implant. This method has also been used for reverse total shoulder arthroplasty (rTSA) baseplate positioning, leading to an unintended superior baseplate inclination. Thus, an rTSA-specific measurement using only the inferior glenoid, termed here the “Reverse Shoulder Correction Angle” (RSCA), was proposed to determine the required inclination. Still, it is unknown if using this correction angle has any appreciable impact on baseplate loading. Thus, the purpose of this basic science study was to compare shoulder biomechanics when baseplates are placed using the TSCA or RSCA method and to identify relationships between these biomechanical effects and variations in scapular anatomy. Methods This study used a previously published modeling workflow that combined statistical shape model, musculoskeletal, and predictive modeling. Thirty scapular morphologies were generated using Latin Hypercube Sampling of the statistical shape model to yield a cohort that replicated normal variations in the population anatomy. A validated musculoskeletal model was modified using each generated morphology, and two virtual surgeries were performed on each model to place the rTSA baseplates: (1) using the TSCA and (2) using the RSCA. Each model underwent muscle-driven predictive simulation of a lateral-reaching task. Joint reaction force (JRF, in % bodyweight) and compression-to-shear force ratio time-series data were statistically tested using statistical parametric mapping paired t -tests. Results Significant differences ( P ≤ .047) were identified in the JRF between the TSCA and RSCA methods. The TSCA method resulted in significantly higher JRFs ( P < .001) across the first 70% of motion because of large superior baseplate shear, with mean load differences in both forces of up to 25% bodyweight in the first 5% of motion. Using the RSCA method resulted in significantly higher JRFs in the last 20% of motion because of high shear and compressive forces, but its compression-to-shear force ratio remained significantly higher than the TSCA. Conclusion The results of this study demonstrate that using the RSCA, rather than the TSCA, to assist with rTSA baseplate positioning results in significantly less challenging loads for baseplate fixation across a motion, thus reducing the likelihood of early baseplate loosening.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".