Functional Outcomes and Shoulder Instability in Reconstruction of Proximal Humerus Metastases
Bibliographic record
Abstract
(1) Background: Some of the goals of orthopedic surgical oncology are saving limbs and function. The humerus is the third most frequent site in primary tumors and one of the most involved sites for metastases. Prosthetic replacement with modular megaprosthesis is one of the treatment choices, but there are several types of complications, such as problems with function and pain. The aim of our study is to assess functional outcomes and shoulder instability in the reconstruction of proximal humerus metastases. (2) Methods: This is a retrospective observational study. Twenty-eight patients, with proximal humerus metastases, admitted to the department of Orthopaedics and Traumatology of our University Hospital between 2014 and 2022 were recruited. Each patient underwent resection and prosthetic replacement surgery with modular megaprosthesis. Clinical evaluation was assessed through MSTS score, WOSI index, and DASH score. (3) Results: Twenty patients were included in the study. Fairly good results, especially regarding pain, function, and emotional acceptance, were obtained in all three tests: DASH, MSTS, and WOSI. Patients who reported shoulder instability actually have worse outcomes than those who report having stable shoulders. In addition, patients with a resection >10 cm have worse outcomes than those who had a resection of 10 cm. No significant differences were found between the deltopectoral approach group and the lateral approach group. (4) Conclusions: Reconstructive surgery with megaprosthesis of the proximal humerus in patients with metastases can be considered a treatment option, especially in patients with pathological fractures or injuries with a high risk of fracture and good life expectancy. This study shows how this type of surgery affects instability, but in terms of functionality, pain, and patient satisfaction, it gives satisfactory results.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".