Antimicrobial prophylaxis in pediatric patients with leukemia: Reducing incidence of febrile neutropenia episodes and bloodborne infections
Bibliographic record
Abstract
IntroductionInfectious complications are a leading cause of morbidity and mortality in pediatric leukemia, particularly during intensive chemotherapy. Febrile neutropenia (FN), bacteremia, and fungemia are common and potentially life-threatening. This study evaluated the effectiveness of antimicrobial prophylaxis in preventing infectious complications in pediatric patients with leukemia.MethodsA retrospective, matched chart review was conducted involving 182 pediatric patients (aged 1-18 years) diagnosed with leukemia. Patients were stratified based on receipt of antimicrobial prophylaxis, Levofloxacin, Caspofungin, Fluconazole, or a combination (n = 40), versus no prophylaxis (n = 64). Primary outcomes included the number of FN episodes, bacteremia, and fungemia.ResultsPatients who received antimicrobial prophylaxis experienced significantly fewer FN episodes and bacteremia events compared to those who did not receive prophylaxis. No cases of increased antimicrobial resistance were observed in the prophylaxis group. Rates of fungemia were low in both groups.ConclusionsAntimicrobial prophylaxis during high-risk phases of chemotherapy is associated with reduced FN and bacteremia in pediatric leukemia patients. These findings support its implementation as a preventative strategy in HR patients to reduce infectious complications without increasing antimicrobial resistance.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".