Shoulder arthroplasty in the management of native shoulder joint infections has a high complication rate and poor functional outcome – a systematic review
Bibliographic record
Abstract
Background: Shoulder arthroplasty is a treatment option of the sequelae of native shoulder joint infections. However, the functional outcomes and re-infection rates are unknown. The aim of this review was to analyse the outcome of shoulder arthroplasty in patients with native shoulder infections. Methods: A review of the online databases MEDLINE and Embase was conducted according to PRISMA guidelines. The review was registered prospectively in the PROSPERO database. Studies reporting either primary or secondary infections of native shoulder joints treated with any form of arthroplasty were included and appraised using the methodological index for non-randomised studies (MINORS) tool. Results: Fourteen studies were eligible for inclusion. Mean age ranged from 56 to 72 years and the mean follow-up from 20.5 months to 8.2 years. Primary shoulder infections were present in 50 patients and secondary infections in 86. 76 patients underwent a two stage: 46 patients a single-stage procedure whilst 14 refused second-stage surgery. Mean post-operative Constant score ranged from 38 to 56.2. The overall reported re-infection rate was 2.3% and complication rate was 26%. Conclusion: Shoulder arthroplasty in the management of either primary or secondary native shoulder infections has a high complication rate and low functional outcome but low re-infection rates at short-term follow-up.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".