Assessment and Management of Febrile Neutropenia in Emergency Departments within a Regional Health Authority—A Benchmark Analysis
Bibliographic record
Abstract
OBJECTIVES: Febrile neutropenia is considered an oncologic emergency, for which prompt initiation of antibiotics is essential. METHODS: We conducted a retrospective cohort study for the 2006 calendar year involving all adult oncology patients presenting with febrile neutropenia to a regional health authority's emergency departments. The objective was to determine the time from triage to antibiotic administration and its impact on patient outcomes. RESULTS: We identified 68 patients presenting with febrile neutropenia, most of whom (76%) were seen in tertiary care centers. Of those patients, 65% were triaged to be seen within 15 minutes of arrival in the emergency room; however, the median time to reassessment was 57 minutes. The median time from triage to antibiotic administration was 5 hours (range: 1.23-22.8 hours). No increased risk of death or increased length of hospital stay was associated with delayed antibiotic administration. Older patients and patients without caregiver support were more likely to experience delayed antibiotic administration (odds ratio: 3.8 and 12.7 respectively). CONCLUSIONS: We were not able to show a deleterious effect of delay in antibiotic administration, but our analysis identified several points at which patient flow through the emergency room could be improved.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".