Effectiveness of antibacterial prophylaxis during induction chemotherapy in children with acute lymphoblastic leukemia
Bibliographic record
Abstract
BACKGROUND: Pediatric patients receiving induction chemotherapy for newly diagnosed acute lymphoblastic leukemia (ALL) are at high risk of developing life-threatening infections. We investigated whether uniform antibacterial guidelines, including mandatory antibacterial prophylaxis in afebrile patients during induction, decreases the incidence of microbiologically documented bacteremia. METHODS: Between 2012 and 2015, 230 patients with newly diagnosed ALL (aged 1-21) were enrolled on Dana-Farber Cancer Institute ALL Consortium Protocol 11-001 (DFCI 11-001). Induction therapy, regardless of risk group, included vincristine, prednisone, doxorubicin, methotrexate, and PEG-asparaginase. Afebrile patients received fluoroquinolone prophylaxis at the initiation of induction and those presenting with fever received broad-spectrum antibiotics; antibiotics were continued until blood count recovery. Rates of documented bacteremias and fungal infections on DFCI 11-001 were compared to those on the predecessor protocol (DFCI 05-001), which included the same induction phase without antibiotic prophylaxis guidelines. RESULTS: Sixty-six (28.7%) patients received fluoroquinolone prophylaxis, the remaining patients received broad-spectrum antibiotics. Twenty-four (36.4%) patients on prophylaxis developed fever and seven (10.6%) developed bacteremia. The overall rate of infection during induction on DFCI 11-001 was lower than on DFCl 05-001 (14.3% vs. 26.3%, P < 0.0001) due to a decreased rate of bacteremia (10.9% vs. 24.4%, P < 0.0001). The rate of fungal infections (4.8% vs. 3.6%) and induction death (0.9% vs. 2%) was not significantly different. CONCLUSION: For children with newly diagnosed ALL, uniform antibiotic administration until blood count recovery, including fluoroquinolone prophylaxis for afebrile patients, reduced the incidence of bacteremia during the induction phase. Larger, randomized studies should be performed to confirm these findings.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".