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Fungal Infections in Neutropenic Patients

2003· book-chapter· en· W4388084218 on OpenAlexaboutno aff
Juan Gea‐Banacloche, Andreas H. Groll, Thomas J. Walsh

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldMedicine
TopicFungal Infections and Studies
Canadian institutionsnot available
Fundersnot available
KeywordsNeutropeniaMedicineAutopsyMucormycosisIntensive care medicineLeukopeniaInternal medicinePathologyChemotherapy

Abstract

fetched live from OpenAlex

Abstract Invasive fungal infections (IFIs) are a significant problem in neutropenic patients. They are common, difficult to diagnose, and associated with high mortality. This chapter is organized according to the clinical, diagnostic, and therapeutic approaches that are recommended for the management of neutropenic patients at risk for IFIs. Although any fungal pathogen may potentially cause infection in neutropenic patients (Walsh and Groll, 1999a), here the focus is on more common fungal infections with special emphasis on Candida spp., Aspergillus spp., Fusarium spp., Scedosporium spp., Trichosporon spp., and the Zygomycetes. The epirical treatment of persistent fever in the neutropenic patient is also reviewed. The initial description of fungal infections in neutropenic patients was published more than 30 years ago (Bodey, 1966a). Bodey reviewed the records of all 454 patients with acute leukemia who died at the National Institutes of Health between 1954 and 1964 and found that 107 patients (24%) had a major fungal infection (excluding focal candidiasis). Despite the changes in clinical practice during the last 4 decades, more recent autopsy studies continue to show high frequencies of IFIs in leukemic patients: 25% overall in a study that included 12 hospitals in Europe, Canada, and Japan (Bodey et al, 1992), and a striking 58% in a more recent European report (Jandrlic et al, 1995). The frequencies are much lower for patients with solid tumors (1%–8%). These numbers must be interpreted with caution because of the selection bias inherent to autopsy studies, and the various definitions used in these series (Krick and Remington, 1976; Fraser et al, 1979; Wingard et al, 1979). The recent large trials of empirical antifungal therapy in neutropenic fever have documented relatively low frequencies of IFI (at the initiation of empirical therapy), ranging between 1.5% and 5.9% (Winston et al, 2000; Boogaerts et al, 2001) and similarly low frequencies of breakthrough infections during treatment, ranging between 2.6% and 5.5% (Boogaerts et al, 2001; Walsh et al, 2002). This broad range of rates reflects in part methodological and definition issues, but also underscores that not all neutropenic patients are at the same risk (Walsh et al, 1994; Prentice et al, 2000). The autopsy studies are also consistent on other points. A large percentage of fungal infections go undiagnosed during life. The general frequencies of pathogens are also fairly similar, with Candida comprising roughly 50% of all cases, Aspergillus 40% and the Zygomycetes, Cryptococcus and other pathogens accounting for the remainder. The current widespread use of azole prophylaxis is probably changing the epidemiology of IFI: Candida albicans is becoming less common, and other more resistant species (C. glabrata, C. krusei) are becoming more prevalent (Abbas et al, 2000; Bodey et al, 2002). At the same time, Aspergillus is emerging as the more common pathogen in some institutions, particularly in the setting of hematopoietic stem cell transplantation, where Aspergillus has replaced Candida as the most common fungal pathogen in a recent series (Martino et al, 2002).

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.019
GPT teacher head0.247
Teacher spread0.229 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

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Citations1
Published2003
Admission routes1
Has abstractyes

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