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Record W4416334223 · doi:10.1128/asmcr.00155-25

Invasive <i>Conidiobolus</i> infection in an immunocompromised pediatric patient in Southern California

2025· article· en· W4416334223 on OpenAlexaff
Jacky Lu, Samuel M. Goodfellow, Esther Vaugon, Andrew Karnaze, Cristina Costales, Michael Smit, Jennifer Dien Bard

Bibliographic record

VenueASM Case Reports · 2025
Typearticle
Languageen
FieldVeterinary
TopicInfectious Diseases and Mycology
Canadian institutionsCentre Hospitalier Universitaire Sainte-Justine
Fundersnot available
KeywordsPosaconazoleNeutropeniaVoriconazoleBacteremiaCellulitisEmergency departmentLeukemia

Abstract

fetched live from OpenAlex

ABSTRACT Background Conidiobolus species are largely known to cause rhinofacial cellulitis in healthy individuals but can manifest as an invasive infection in immunocompromised patients. Few cases have been described in the literature as they are difficult to diagnose and rarely observed in North America, particularly in the pediatric population. Case Summary A 15-year-old boy with a history of multiple bone marrow transplants for mixed phenotype acute leukemia presented to the emergency department with febrile neutropenia and chills. His clinical course was complicated by potential Rothia mucilaginosa bacteremia, but imaging revealed a potential invasive pulmonary fungal infection. Following rapid decompensation and admission in the intensive care unit, plasma cell-free DNA sequencing was ordered, identifying Conidiobolus species. The same organism was then isolated in the patient’s sputum culture shortly thereafter. This organism was found to demonstrate high minimum inhibitory concentration to antifungals tested in vitro . Ultimately, the patient was discharged on posaconazole and trimethoprim-sulfamethoxazole. Conclusion Conidiobolus can be an agent of invasive fungal infection and may be considered a potential infectious agent in the immunocompromised patient. Treatment and outcomes of invasive Conidiobolus remain elusive.

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 score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.014
GPT teacher head0.283
Teacher spread0.269 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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