Management and outcome of febrile neutropenia in admitted presumed immunocompetent patients with suspected viral illness
Bibliographic record
Abstract
Objectives: Febrile neutropenia (FN) creates concern in paediatrics due to the risk of serious bacterial infections (SBI). Protocols with empiric antibiotics designed for hematology and oncology are often applied in healthy children with FN despite lower rates of SBI in this population. This study quantifies rates of infections in presumed immunocompetent children hospitalized with suspected viral illnesses and FN. Methods: This was a retrospective chart review of healthy children admitted to the Stollery Children's Hospital between 2007 and 2017 with fever, absolute neutrophil counts < 0.5 × 109/L, and viral symptoms. Primary outcomes were the incidence of SBI and bacterial pneumonia. Results: and one for Coryneforms, all considered contaminants. There were three urinary tract infections and two pneumonias. Eighty-three per cent of patients had normalization of neutrophil counts, with a median neutropenia duration of 3.2 months. Follow-up diagnoses included chronic benign neutropenia of childhood (N = 17) and three rheumatologic/autoimmune conditions (N = 3). Conclusion: Our results support previous findings of low rates of invasive bacterial infections in healthy children with FN. With an SBI rate of 3.1% and few patients found to have any pathologic etiology for their neutropenia, prospective studies would be valuable to evaluate the need for a practice change regarding antibiotic use in low-risk patients with suspected viral-induced neutropenia.
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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 | 0.000 | 0.004 |
| 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".