Use of custom glenoid components for reverse total shoulder arthroplasty
Bibliographic record
Abstract
BACKGROUND: Our purpose was to evaluate a custom reverse total shoulder arthroplasty glenoid baseplate for severe glenoid deficiency, emphasizing the challenges with this approach, including short-term clinical and radiographic outcomes and complications. METHODS: This was a single-institution, retrospective series of 29 patients between January 2017 and December 2022 for whom a custom glenoid component was created for extensive glenoid bone loss. Patients were evaluated preoperatively and at intervals for up to 5 years. All received preoperative physical examinations, plain radiographs, and computed tomography (CT). Intra- and postoperative complications are reported. RESULTS: Of 29 patients, delays resulted in only undergoing surgery, and in three of those, the implant did not match the glenoid. For those three, the time from CT scan to implantation averaged 7.6 months (range, 6.1-10.7 months), compared with 5.5 months (range, 2-8.6 months) for those whose implants fit. In patients with at least 2-year follow-up (n=9), no failures occurred. Significant improvements were observed in all patient-reported outcome measures in those nine patients (American Shoulder and Elbow Score, P<0.01; Simple Shoulder Test, P=0.02; Single Assessment Numeric Evaluation, P<0.01; Western Ontario Osteoarthritis of the Shoulder Index, P<0.01). Range of motion improved for forward flexion and abduction (P=0.03 for both) and internal rotation up the back (P=0.02). Pain and satisfaction also improved (P<0.01 for both). CONCLUSIONS: Prolonged time (>6 months) from CT scan to device implantation resulted in bone loss that rendered the implants unusable. Satisfactory short-term radiographic and clinical follow-up can be achieved with a well-fitting device. Level of evidence: III.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".