Quality of febrile neutropenia management in a regional cancer center emergency department.
Bibliographic record
Abstract
104 Background: ASCO's current guidelines for febrile neutropenia (FN) management support antibiotic administration within one hour of presentation to the emergency department (ED). Prompt initiation of antibiotic therapy is vital to decrease the likelihood of adverse outcomes. Many studies, however, have reported significant delays in antibiotic initiation with mean wait times far exceeding ASCO's guidelines. We aimed to assess the quality of FN management at a regional cancer centre ED. Methods: Patients undergoing chemotherapy who visited the ED at the Peel Regional Cancer Center in Ontario, Canada between 04/12 - 03/13 were identified using electronic medical records. Patients were excluded if there was no record of chemotherapy delivery within 30 days prior to ED visit. ICD-10 codes and chart data were used to identify patients who had presented for either fever or infection. The primary outcome measures were three major quality of health indicators; time to assessment by a physician, Canadian Triage and Acuity Scale (CTAS) score, and time to initiation of intravenous antibiotics. Results: In total 239 records were included in the analysis. CTAS score was concordant with recommendation for FN (level 1-2) in 85% of patients and did not vary based on primary cancer site (p = 0.17). The mean time to physician assessment was 97.2 min and the mean time to initiation of IV antibiotics was 194.7 min. Overall, 14.6% of patients received their first dose of antibiotic therapy within the recommended 1 hour window. Conclusions: Our audit identified a large margin for improvement in the time to initiation of antibiotic therapy for chemotherapy patients with suspected FN. Prompt recognition and initiation of standardized treatment pathways for FN in the ED may improve the time to initiation of antibiotic therapy. In an attempt to address this gap in quality we have developed and distributed a standardized wallet-sized fever card to all patients receiving cytotoxic chemotherapy within our regional cancer program. This card contains information pertaining to the current chemotherapy treatment and recommended ED treatment protocols for FN. An evaluation of the impact of these cards is ongoing.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".