Invasive fungal diseases in children with hematological malignancies and stem cell transplant recipients
Bibliographic record
Abstract
Invasive Fungal Disease (IFD) remains a major cause of morbidity and mortality in patients with hematological malignancies or undergoing stem cell transplantation. New surrogate markers of IFD - galactomannan antigen, β -D-glucan, fungal polymerase chain reaction… - and the availability of modern antifungal drugs have changed the management of this serious complication, but both have limitations in children. There are still some unclear issues, particularly in pediatric patients, regardless of the medical and economic possibilities of hospitals: IFD remains difficult to diagnose in a timely way, stem cell transplant recipients have different risk of IFD, antifungal drugs are not free from adverse effects and prospective studies in children are scarce. In this sense, diagnosis criteria have to be assessed , risk stratification should be redefined and the different types of prophylaxis/treatment -prophylaxis, preemptive, empiric and targeted treatment- must be used correctly. We consider low risk for IFD children undergoing autologous stem cell transplantation and children with acute lymphoblastic leukemia, both should receive fluconazole as primary prophylaxis and empiric therapy with an agent with activity against mold. In those undergoing allogeneic stem cell transplantation or chemotherapy for acute myeloid leukemia, prophylaxis should be performed with a mold active agent -triazoles priority-. We rarely use preemptive therapy because of a high false positive rate of galactomannan. In proven and probable IFD we use targeted therapy, selecting a drug based on the type of infection, sensitivity to the drug in our hospital and prior antifungal prophylaxis/treatment. In selected patients, antifungal combination therapy should be a valid option. Despite great advances, there are still thought provoking questions on the diagnosis and management of IFD in children with hematological diseases.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".