Fungal periprosthetic joint infection following total elbow arthroplasty: a case report and review of the literature
Bibliographic record
Abstract
BACKGROUND: With improving surgical techniques for total elbow arthroplasty clinical outcomes have improved and its utilization continues to increase. Despite these advances, complication rates remain as high as 24%. Of these complications periprosthetic joint infection is one of the most common and morbid. The rheumatoid elbow remains a leading indication for total elbow arthroplasty. Patients with this condition frequently require immunosuppressive therapy, which places them at higher risk of both typical and atypical infections. CASE PRESENTATION: We present the case of a persistent, late-onset periprosthetic joint infection in a total elbow arthroplasty of a 64-year-old Caucasian woman with severe refractory rheumatoid arthritis. The offending pathogen, Aspergillus terreus, is previously unreported in the arthroplasty literature and grew concurrently with coagulase-negative staphylococcus. Eradication of the fungal and bacterial agents involved resection arthroplasty, serial debridement, and multiple courses of intravenous and oral antimicrobial therapy. Two attempts at reimplantation arthroplasty failed to eliminate the infection and our patient ultimately required definitive resection arthroplasty. CONCLUSIONS: Arthroplasty in the rheumatoid elbow confers with it a high complication rate. Inflammatory disease and immunosuppressive drugs combined with the subcutaneous anatomy of the elbow contribute to the risk of infection. Fungal periprosthetic joint infection in the rheumatoid patient presents both diagnostic and therapeutic challenges. Fungal growth should always be treated and requires organism-specific antimicrobials in conjunction with surgical debridement. More literature is needed to determine the optimal treatment regimen for this devastating complication.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".