Pediatric Benign Neutropenia: Assessing Practice Preferences in Canada
Bibliographic record
Abstract
Pediatric benign neutropenia is a self-limited condition with a benign clinical course. An approach to this condition is not well-defined in the literature. Our objective was to use a case-based survey to elucidate trends in the diagnosis and management of benign neutropenia among pediatric hematology/oncology practitioners in Canada. We received 46 completed surveys (response rate 66%). At initial presentation with fever and neutropenia, 67% of respondents recommended partial septic workup but 11% recommended no investigations. Nearly 70% recommended admission for empiric intravenous antibiotics, while 24% would discharge home without antibiotics. In a patient with fever and known neutropenia, respondents were more likely to pursue outpatient antibiotic therapy. For investigation of chronic neutropenia, most respondents (60%) do not use antineutrophil antibody testing. Common indications for bone marrow biopsy were severe infection, prolonged neutropenia, or before initiating granulocyte colony stimulating factor. Indications for granulocyte colony stimulating factor were based on severity and frequency of infection. Most respondents (84%) would not recommend antibiotic prophylaxis. Results demonstrate the considerable variability in management of benign neutropenia among pediatric hematology/oncology practitioners in Canada and highlight the need for prospective studies to establish diagnostic criteria for benign neutropenia and evaluate management of fever in this population.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".