Errors in implant orientation estimation in novice vs. experienced surgeons during reverse shoulder arthroplasty for a superior glenoid wear pattern
Bibliographic record
Abstract
Background: Glenoid baseplate orientation in reverse shoulder arthroplasty influences clinical outcomes, complications, and failure rates. This study aimed to determine novice and experienced shoulder surgeon's ability to accurately characterize glenoid component orientation in an intraoperative scenario. Methods: Glenoid baseplates were implanted in 8 fresh frozen cadavers by novice surgical trainees. Glenoid baseplate version, inclination, augment rotation, and superior-inferior center of rotation offset were then measured using in-person visual assessments by novice and experienced shoulder surgeons immediately after implantation. Glenoid orientation parameters were then measured using 3-dimensional (3D) computed tomography (CT) scans with digitally reconstructed radiographs (DRRs) by 2 independent observers with a 1-month time interval between repeat measurements. Bland-Altman plots were produced to determine the accuracy of glenoid orientation using standard intraoperative assessment compared to postoperative 3D CT scan results. Interclass correlation coefficients were produced for measurements, rated as 0.01-0.39 poor, 0.40-0.59 fair, 0.60-0.74 good, and 0.75-1.00 excellent. Results: = .50) occurred. Experienced surgeons had greater measurement error than novices for all measured parameters. Conclusion: Intraoperative measurement errors in glenoid placement are present for both inexperienced and experienced surgeons. Kinesthetic input during implantation may improve orientation understanding.
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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".