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Record W2054927354 · doi:10.1086/320514

Association between Antifungal Prophylaxis and Rate of Documented Bacteremia in Febrile Neutropenic Cancer Patients

2001· article· en· W2054927354 on OpenAlexfundno aff
Claudio Viscoli, Marianne Paesmans, Miguel Á. Sanz, Elio Castagnola, Jean Klášterský, P. Martino, M. P. Glauser

Bibliographic record

VenueClinical Infectious Diseases · 2001
Typearticle
Languageen
FieldMedicine
TopicNeutropenia and Cancer Infections
Canadian institutionsnot available
FundersHospital for Sick ChildrenBrown University
KeywordsMedicineBacteremiaFebrile neutropeniaAntifungalInternal medicineFungemiaNeutropeniaIntensive care medicineChemotherapyAntibioticsMicrobiologyDermatology

Abstract

fetched live from OpenAlex

Published data have suggested a correlation between antifungal prophylaxis and bacteremia in febrile neutropenia. This correlation was investigated among 3002 febrile neutropenic patients enrolled in 4 trials during 1986-1994. Globally, 1322 patients (44%) did not receive antifungal prophylaxis; 835 (28%) received poorly absorbable antifungal agents and 845 (28%) received absorbable antifungal agents. The rates of bacteremia for these groups were 20%, 26%, and 27%, respectively (P=.0001). In a multivariate model without including antifungal prophylaxis, factors associated with bacteremia were: age, duration of hospitalization, duration of neutropenia before enrollment, underlying disease, presence of an intravenous catheter, shock, antibacterial prophylaxis, temperature, and granulocyte count at onset of fever. When antifungal prophylaxis was included, the adjustment quality of the model improved slightly (P=.05), with an odds ratio of 1.19 (95% confidence interval [CI], 0.92-1.55) for patients receiving nonabsorbable and 1.42 (95% CI, 1.07-1.88) for those who were receiving absorbable antifungal agents. Antifungal prophylaxis with absorbable agents might have an impact on the rate of documented bacteremia in febrile neutropenia. This effect should be confirmed prospectively.

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)0.001
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 score0.007
Threshold uncertainty score0.535

Codex and Gemma teacher scores by category

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

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 head0.023
GPT teacher head0.353
Teacher spread0.330 · 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

Citations33
Published2001
Admission routes1
Has abstractyes

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