Microbiology and Mortality of Pediatric Febrile Neutropenia in El Salvador
Bibliographic record
Abstract
BACKGROUND: Febrile neutropenia (FN) and infection-related mortality are major problems for children with cancer in low-income countries. Identifying predictors for adverse outcome of FN in low-income countries permits targeted interventions. We describe the nature and predictors of microbiologically documented infection (MDI) and mortality of FN in children with cancer in El Salvador. METHODS: We examined Salvadoran pediatric oncology patients admitted with FN over a 1-year period. Data were collected prospectively. Demographic, treatment, and admission-related variables were examined as predictors of outcomes. RESULTS: Hundred six FN episodes among 85 patients were included. Twenty-three of 106 episodes (22%) were microbiologically documented; 13 of 106 episodes (12%) resulted in death. Gram-positive and gram-negative organisms were isolated in 14 of 23 and 11 of 23 specimens; polymicrobial infections were common (11 of 23 episodes of MDI). Older age decreased the MDI risk [odds ratio (OR) per year=0.87, 95% confidence interval (CI), 0.75-0.99; P=0.04] while increasing number of days since the last chemotherapy increased the risk (OR=1.03 per day, 95% CI, 1.01-1.04; P=0.002). Pneumonia diagnosed either clinically (OR=6.6, 95% CI, 1.8-30.0; P=0.005) or radiographically (OR=5.5, 95% CI, 1.7-18.1; P=0.005) was the only predictor of mortality. CONCLUSIONS: In El Salvador, polymicrobial infections were common. Pneumonia at admission identified children with FN at high risk of death; these children may benefit from targeted interventions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".