<i>Mycobacterium fortuitum</i> peritoneal dialysis-related peritonitis in a child: A case report and review of the literature
Bibliographic record
Abstract
BACKGROUND: Non-tuberculous mycobacteria (NTM) are an uncommon but serious cause of peritoneal dialysis (PD)–related infections. NTM peritonitis typically necessitates PD catheter removal, PD withdrawal, and aggressive, prolonged antimicrobial treatment. Few reported cases of NTM peritonitis in the pediatric population exist. METHODS: We describe a case of a 9-year-old boy on PD after kidney allograft failure who developed Mycobacterium fortuitum peritonitis, and we summarize the available literature on M. fortuitum peritonitis in pediatric patients receiving PD. RESULTS AND CONCLUSION: Therapeutic options were limited by adverse medication effects and risk of drug–drug interactions in a patient with complex mental health comorbidities. Clofazimine presented an acceptable oral treatment option for long-term therapy in combination with ciprofloxacin and was well tolerated by this patient. Prompt PD catheter removal followed by 6 months of dual antimicrobial therapy resulted in a full recovery and successful re-transplantation with no infection relapse.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".