Risk Factors for Invasive Fungal Disease in Pediatric Cancer and Hematopoietic Stem Cell Transplantation: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Although a number of risk factors have been associated with invasive fungal disease (IFD), a systematic review of the literature to document pediatric-specific factors has not been performed. METHODS: We used the Ovid SP platform to search Medline, Medline In-Process, and Embase for studies that identified risk factors for IFD in children with cancer or those who undergo hematopoietic stem cell transplantation (HSCT). We included studies if they consisted of children or adolescents (<25 years) who were receiving treatment for cancer or undergoing HSCT and if the study evaluated risk factors among patients with and those without IFD. RESULTS: Among the 3566 studies screened, 22 studies were included. A number of pediatric factors commonly associated with an increased risk for IFD were confirmed, including prolonged neutropenia, high-dose steroid exposure, intensive-timing chemotherapy for acute myeloid leukemia, and acute and chronic graft-versus-host disease. Increasing age, a factor not commonly associated with IFD risk, was identified as a risk factor in multiple published cohorts. CONCLUSIONS: With this systematic review, we have confirmed IFD risk factors that are considered routinely in daily clinical practice. Increasing age should also be considered when assessing patient risk for IFD. Future efforts should focus on defining more precise thresholds for a particular risk factor (ie, age, neutropenia duration) and on development of prediction rules inclusive of individual factors to further refine the risk prediction.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".