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Record W1979952460 · doi:10.3747/co.v18i6.841

Assessment and Management of Febrile Neutropenia in Emergency Departments within a Regional Health Authority—A Benchmark Analysis

2011· article· en· W1979952460 on OpenAlexaffvenue
David Szwajcer, Piotr Czaykowski, Donna Turner

Bibliographic record

VenueCurrent Oncology · 2011
Typearticle
Languageen
FieldMedicine
TopicNeutropenia and Cancer Infections
Canadian institutionsUniversity of ManitobaCancerCare Manitoba
Fundersnot available
KeywordsMedicineFebrile neutropeniaNeutropeniaEmergency departmentBenchmark (surveying)Intensive care medicineMedical emergencyNursingInternal medicine

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.612

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.174
GPT teacher head0.463
Teacher spread0.289 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations36
Published2011
Admission routes2
Has abstractyes

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