Continental and demographic analysis of preoperative measurements in shoulder arthroplasty from a large-scale dataset using Materialise software for surgical planning
Bibliographic record
Abstract
Background: Preoperative planning software is increasingly used in shoulder arthroplasty to optimize implant placement. This study aims to describe the methods used in Materialise's planning software for critical measurement parameters to provide a benchmark for cases planned using Materialise software, and to analyze the demographics and characteristics of patients undergoing reverse shoulder arthroplasty (rTSA) and anatomic total shoulder arthroplasty (aTSA) across different continents and sexes. Methods: A dataset of 11,509 surgeon-approved planning cases (2018-2023) from the TRUMATCH Personalized Solutions Shoulder System was analyzed. The software automatically generates three-dimensional models from computed tomography scans and measures glenoid version, inclination, humeral subluxation, glenoid vault loss, and maximum erosion depth. A univariate statistical analysis, including Mann-Whitney U test and Kruskal-Wallis H test with Bonferroni correction, was conducted to evaluate differences across surgery types (rTSA vs. aTSA), sex, and continents (North America, Europe, Oceania). Results: The population had an average age of 69.9 years, with 65.7% undergoing rTSA and 34.3% undergoing aTSA. Significant differences were observed between rTSA and aTSA patients in terms of age, version angle, inclination angle, erosion depth, and humeral head diameter. Females accounted for 53.1% of the dataset and were generally older, with less retroversion and subluxation compared to males. Regionally, 61% of procedures were performed in North America, 25% in Europe, and 14% in Oceania. rTSA was more common than aTSA across all continents, with the highest proportion found in Europe (86%). Significant continental differences were noted in age, version angle, subluxation, and humeral head diameter. Conclusion: This study provides a comprehensive methodology for preoperative measurements using Materialise software and highlights significant demographic and continental differences in shoulder arthroplasty parameters. These findings underscore the need for standardized measurement methods and suggest that continental and sex-specific factors influence the choice between rTSA and aTSA. Future research should focus on correlating surgical plans with outcomes to optimize treatment strategies tailored to individual patient needs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".