Microbiologically documented infections and infection-related mortality in children with acute myeloid leukemia
Bibliographic record
Abstract
The primary objective was to describe the prevalence and characteristics of microbiologically defined infections and infection-related mortality (IRM) in 492 children with acute myeloid leukemia enrolled on CCG 2961. Secondary objectives were to determine the relationship between demographic, disease-related, and therapeutic variables, and infections and IRM. Institutions documented infections prospectively. Age, ethnicity, body mass index, leukemia karyotype, treatment, and institutional size were examined for association with infection outcomes. More than 60% of children experienced such infections in each of 3 phases of chemotherapy. There were 58 infectious deaths; cumulative incidence of IRM was 11% plus or minus 2%. Thirty-one percent of infectious deaths were associated with Aspergillus, 25.9% with Candida, and 15.5% with alpha hemolytic streptococci. Age older than 16 years (hazard ratio [HR], 3.32; 95% confidence interval [CI], 1.87-5.89; P < .001), nonwhite ethnicity (HR, 1.85; 95% CI, 1.10-3.09; P = .02), and underweight status (HR, 3.06; 95% CI, 1.51-6.22; P = .002) were associated with IRM, while size of the treating institution was not. Thus, age, ethnicity, and BMI were important contributors to IRM. Fungi and Gram-positive cocci were the most common organisms associated with IRM and, in particular, Aspergillus species was the largest contributor to infectious deaths.
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".