<p>The Personalized Shoulder Reconstruction Assisted by 3D Printing Technology After Resection of the Proximal Humerus Tumours</p>
Bibliographic record
Abstract
BACKGROUND: The reverse shoulder arthroplasty (RSA) may be a promising alternative for proximal humerus tumours because of good postoperative shoulder function. However, the conventional reverse shoulder prosthesis can not meet individual needs and RSA has been associated with a relatively high complication rate. Therefore, implant design and surgical reconstruction technique warrant further study. METHODS: Between September 2015 and May 2018, 7 patients were treated via RSA after en-bloc resection of the proximal humerus tumours. A 3D-printed guiding baseplate was used to assist the implant of the 3D-printed glenoid prosthesis; a personalized humerus prosthesis was used to reconstruct the proximal humerus. The functional outcomes were assessed by range of motion (ROM) of the shoulder joint, Musculoskeletal Tumour Society (MSTS) functional score, and Toronto Extremity Salvage Score (TESS). We also analyzed tumour recurrence, metastases, and complications associated with the reconstruction procedure. RESULTS: All patients were observed for 14 to 36 months, with an average of 23.6 months. At the final follow-up, the mean MSTS score was 85.7% (range, 73.3-93.3%), and the mean TESS score was 90.0% (range, 84.1-95.9%). No instability, infection, scapular notching, loosening or fracture were observed in this series. One patient with GCT suffered from pulmonary metastasis, while one with osteosarcoma died because of pulmonary metastasis. CONCLUSION: The 3D-printed guiding baseplate facilitated the accurate implantation of the glenoid prosthesis. The RSA based on a 3D-printed glenoid prosthesis and a personalized custom-made humerus prosthesis significantly improved the shoulder function and decreased the complication rate. Further studies of a larger scale with longer follow-up are required to validate this technology.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".