MétaCan
Menu
Back to cohort
Record W2553977523 · doi:10.1093/mmy/myw118

Successful treatment of<i>Aspergillus</i>ventriculitis through voriconazole adaptive pharmacotherapy, immunomodulation, and therapeutic monitoring of cerebrospinal fluid (1→3)-β-D-glucan

2016· review· en· W2553977523 on OpenAlexfundno aff
Tempe K. Chen, Paula K. Groncy, Ramin J. Javahery, Richard Y. Chai, Pablito G. Nagpala, Malcolm Finkelman, Rūta Petraitienė, Thomas J. Walsh

Bibliographic record

VenueMedical Mycology · 2016
Typereview
Languageen
FieldMedicine
TopicAntifungal resistance and susceptibility
Canadian institutionsnot available
FundersSick Kids Foundation
KeywordsVentriculitisVoriconazoleAspergillosisMedicinePharmacotherapyAspergillusGerminomaMeningoencephalitisCerebrospinal fluidTherapeutic drug monitoringMeningitisImmunologyInternal medicinePharmacologyBiologySurgeryDrugMicrobiologyChemotherapy

Abstract

fetched live from OpenAlex

Aspergillus ventriculitis is an uncommon but often fatal form of invasive aspergillosis of the central nervous system (CNS). As little is known about the diagnosis, treatment, and outcome of this potentially lethal infection, we report the strategies used to successfully treat Aspergillus ventriculitis complicating a pineal and pituitary germinoma with emphasis on the critical role of adaptive pharmacotherapy of voriconazole and serial monitoring of (1→3)-β-D-glucan in cerebrospinal fluid. We describe several rationally based therapeutic modalities, including adaptive pharmacotherapy, combination therapy, sargramostim-based immunomodulation, and biomarker-based therapeutic monitoring of the CNS compartment. Through these strategies, our patient remains in remission from both his germinoma and Aspergillus ventriculitis making him one of the few survivors of Aspergillus ventriculitis.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.052
GPT teacher head0.380
Teacher spread0.329 · 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 designCase report
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
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueMedical MycologySame topicAntifungal resistance and susceptibilityFrench-language works237,207