Second Bacteremia During Antibiotic Treatment in Children With Acute Myeloid Leukemia: A Report From the Canadian Infections in Acute Myeloid Leukemia Research Group
Bibliographic record
Abstract
BACKGROUND: The risk of second bacteremia during antibiotic treatment for initial bacteremia is unknown in high-risk populations. Our objectives were to describe the prevalence of second bacteremia during treatment and identify risk factors in children with acute myeloid leukemia (AML). METHODS: We conducted a retrospective, population-based cohort study that included children and adolescents with de novo, non-M3 AML who were diagnosed and treated between January 1, 1995 and December 31, 2004 at 15 Canadian centers. Patients were monitored for bacteremia during chemotherapy until completion of treatment, hematopoietic stem cell transplantation, relapse, refractory disease, or death. RESULTS: There were 290 episodes of bacteremia occurring in 185 (54.3%) of 341 children. Eighteen (6.2%) had a second bacteremia while receiving antibiotic treatment. Two episodes of second bacteremia were complicated by sepsis; there were no infection-related deaths. Eleven episodes (61.1%) had either an initial Gram-positive and subsequent Gram-negative bacteremia or initial Gram-negative followed by Gram-positive bacteremia. Days receiving corticosteroids (odds ratio [OR], 1.09; 95% confidence interval [CI], 1.07-1.12; P < .0001), cumulative dose of corticosteroids (OR, 1.04; 95% CI, 1.00-1.08; P = .035), and days of neutropenia from start of course to initial bacteremia (OR, 1.07; 95% CI, 1.02-1.12; P = .007) were significantly associated with second bacteremia. CONCLUSIONS: In pediatric AML, 6% of patients will experience a second bacteremia during antibiotic treatment; duration of corticosteroid exposure and neutropenia are risk factors. These patients remain at high risk for second bacteremia after identification of the initial bacteremia and warrant continued broad-spectrum treatment during profound neutropenia.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".