Prosthetic Knee Joint Infection Caused by Mycobacterium kansasii
Bibliographic record
Abstract
Mycobacterium kansasii is a nontuberculous mycobacterium that is a rare cause of prosthetic joint infections (PJIs). This case report presents a 58-year-old man who developed rapidly progressive arthritis after exposing his right knee to an unknown fluid at a microbial pharmaceutical company. Within a year, he underwent a right total knee arthroplasty (TKA). At 5 months postoperatively, he presented with pain and swelling of that knee. Imaging revealed extensive periprosthetic osteolysis with diffuse intracapsular and posterior extracapsular fluid collections. Multiple knee aspirates had negative cultures, and infectious laboratory test results were equivocal. Two years after his primary arthroplasty, the patient underwent posterior débridement and one-stage revision TKA with antibiotic cement. Synovial fluid mycobacterial cultures aspirated 2 weeks before the revision surgery became positive on postoperative day 1. PCR identified M kansasii. At 3 weeks postoperatively, intraoperative periprosthetic cultures grew mycobacterium. M kansasii was confirmed using mass spectrometry. Once susceptibilities returned, the patient was treated with targeted antimycobacterial therapy. This case report demonstrates the importance of considering atypical PJI in painful TKA with negative cultures and equivocal laboratory results. In the future, when there is concern for an atypical PJI, molecular diagnostic tools and mycobacterial cultures should be used before surgical intervention.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".