Invasive fungal infections in critically ill children: epidemiology, risk factors and antifungal drugs.
Bibliographic record
Abstract
Background: are also emerging because of the empirical use of antifungal drugs. This updated review discusses the epidemiology of IFIs as well as antifungal drugs, dosing and potential adverse effects in critically ill children. Methods: A PubMed search was conducted with Clinical Queries using the key terms "antifungal", "children", "critical care" AND "paediatric intensive care unit" OR "PICU". The search strategy included clinical trials, randomized controlled trials, meta-analyses, observational studies and reviews and was limited to the English literature in paediatrics. Results: spp. associated with paediatric ICIs. The risks and epidemiology for IFIs vary if considering previously healthy children treated in the paediatric intensive care unit or children with leukaemia, malignancy or a severe haematological disease. The mortality rate for IAIs in children is 2.5-3.5-fold higher than for ICIs. Four major classes of antifungals for critically ill children are azoles, polyenes, antifungal antimetabolites and echinocandins. Conclusions: Special Issue: https://www.drugsincontext.com/special_issues/challenges-and-strategies-in-the-management-of-invasive-fungal-infections.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".