Fungal Infection following Total Elbow Arthroplasty
Bibliographic record
Abstract
A specific treatment protocol for managing fungal infections after total elbow arthroplasty (TEA) does not currently exist. The purpose of this report is to describe our experience and outline our treatment algorithm for a rare case of prosthetic joint infection (PJI) following a TEA. We present a case of a PJI due to Candida parapsilosis after TEA in a 57 year-old Caucasian woman with a history of hypertension, depression, and three previous surgical procedures to the affected limb. A fungal PJI by the organism C. parapsilosis following TEA has not been previously reported. Successful eradication of the fungal infection was achieved utilizing resection arthroplasty; placement of an amphotericin, vancomycin, and tobramycin-impregnated cement spacer; and 6 months of organism-specific antifungal medication. Although the patient was clinically ready for reimplantation, she passed away due to unrelated issues before reimplantation could be performed. While PJI is a devastating complication following TEA, a fungal infection is a rare complication that imposes difficult challenges to the treating surgeon. With our case report, we hope to contribute to the overall knowledge of fungal infections associated with TEA and describe our successful treatment of this complex case.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".