A Retrospective Analysis of Invasive Fungal Diseases (IFD) of the Central Nervous System in Children With Lymphoid Malignancies
Bibliographic record
Abstract
BACKGROUND: Outcomes of childhood hematolymphoid malignancies have improved several fold because of immunosuppressive chemotherapy and broad-spectrum antibiotics for managing febrile neutropenia. An apparent trade-off has been an increase in invasive fungal disease (IFD), affecting multiple organs. We report the diagnostic and therapeutic challenges in 8 children with lymphoid cancers who developed intracranial (IC) fungal abscesses between 2010 and 2017. METHODS: Children below 15 years of age undergoing treatment for leukemia/lymphoma with clinicoradiologic and microbiologic evidence of IC fungal abscess were included. Demographic details, clinical profile, and management were retrospectively audited. Treatment was guided by European Organization for Research and Treatment of Cancer/Mycoses Study Group (EORTC/MSG) definitions for IFD with therapeutic drug monitoring (TDM)-directed azole dosing, and surgical intervention. RESULTS: Eight patients (4 B-cell acute lymphoblastic leukemia, 2 relapsed B-cell acute lymphoblastic leukemia, and 2 non-Hodgkin lymphoma) were eligible for analysis. Proven, probable, and possible IFDs were seen in 2 (25%), 4 (50%), and 2 (25%) patients, respectively. Proven IFDs were invasive mucormycosis with remaining having mold infections. Cerebrospinal fluid galactomannan was positive in all 4 patients in whom it was tested. TDM was possible in 5/8 (63%) patients. Antifungal therapy was given for a median period of 4.2 months with 5 (63%) patients having complete resolution. Three (37%) patients expired, of which 2 were attributable to IFDs. CONCLUSIONS: IC fungal abscesses in children can cause significant morbidity and mortality in children with hematolymphoid cancers. Evaluation of cerebrospinal fluid galactomannan may help in early diagnosis and therapy. Prolonged antifungal therapy steered by TDM can help achieve resolution in some cases.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".