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Record W2799888899 · doi:10.1111/tid.12913

Fungal infections in hematopoietic stem cell transplantation in children at a pediatric children’s hospital in Argentina

2018· article· en· W2799888899 on OpenAlexaff
Sergio Gomez, Miguela A. Caniza, Alicira Fynn, Cecilia Vescina, Claudia Ruiz, Daniela Iglesias, Fernanda Mariel Sosa, Lillian Sung

Bibliographic record

VenueTransplant Infectious Disease · 2018
Typearticle
Languageen
FieldMedicine
TopicAntifungal resistance and susceptibility
Canadian institutionsHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsMedicineHematopoietic stem cell transplantationIncidence (geometry)Retrospective cohort studyAspergillosisCumulative incidenceComplicationPediatricsTransplantationHematopoietic stem cellInternal medicineStem cellHaematopoiesisImmunology

Abstract

fetched live from OpenAlex

Our primary objective was to describe the incidence of proven or probable invasive fungal infections (IFIs), a devastating complication of hematopoietic stem cell transplant (HSCT), in HCST in a middle-income country. Secondary objectives were to describe factors associated with IFIs and outcomes. In this single center retrospective study, pediatric patients who underwent a first allogeneic or autologous HSCT from 1998 to 2016 were included. Of the 251 HSCT recipients: 143 transplants were allogeneic and 108 were autologous. Overall, 23 (9%) experienced an IFI, mostly due to yeasts (83%). IFIs were more common in allogeneic HSCT (18/143, 13%) than in autologous HSCT (5/108, 5%; P = .045). Of the 23 patients with IFIs, 14 (61%) died, but only 1 directly from IFI (pulmonary aspergillosis). Overall survival at 3 years was 0.42 ± 0.11 in patients with IFIs and 0.60 ± 0.37 in those without IFIs (P = .049). In Argentina, IFIs during HSCT are common. Recipients of allogeneic HSCT are at higher risk, and IFI is associated with reduced overall survival. Future work should focus on interventions to reduce and improve IFI outcomes in children undergoing transplants in low- and middle-income countries.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.005
GPT teacher head0.223
Teacher spread0.218 · 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.

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

Citations19
Published2018
Admission routes1
Has abstractyes

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