Mucormycosis in a Pediatric Oncology Population - Review of Cases and the Literature
Bibliographic record
Abstract
Background: Mucormycosis is the third most common invasive fungal infection in children and primarily affects immunocompromised patients with cancer. Incidence rates and contemporary survival statistics are needed in view of current cancer management protocols, availability of molecular tests and newer antifungals. Methods: A retrospective chart review of cases of mucormycosis in patients with oncologic diagnoses at the Children’s Hospital of Eastern Ontario, in Ottawa, Canada, between 2000 and 2020 was completed. We describe the clinical characteristics, diagnosis, treatment, and outcomes and inform areas for future research. Results: Over 20 years, the incidence rate among hematology-oncology patients was 0.66% with four cases identified. The underlying diagnosis in these cases was ALL(n=3) and AML(n=1). The average age at diagnosis of mucormycosis was 7 years. The sites of infection were cutaneous (perianal), disseminated, rhino-orbito-cerebral and pulmonary. All patients were receiving induction chemotherapy at the time of infection. Most were on high-dose steroids(n=3) and antibiotics(n=3), and half(n=2) were on antifungal prophylaxis. Mucormycosis was diagnosed using histopathological, culture and/or PCR results. Rhizopus species was most commonly isolated. ABLC was the mainstay of treatment, but all patients received combination antifungal therapy. Two patients underwent surgical debridement/resection. All cases had delays in their cancer treatment secondary to infection. Mucormycosis-related mortality was 25 percent. Conclusions: Mucormycosis has a high morbidity and mortality affecting immunosuppressed individuals. Given its rarity and heterogeneity in clinical presentation, diagnosis is often delayed. This case series shows that with early diagnosis, aggressive anti-fungal therapy with possible adjunctive surgical intervention, positive patient outcomes can be achieved.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".