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H-ficolin serum concentration and susceptibility to fever and neutropenia in paediatric cancer patients

2009· article· en· W2093471951 on OpenAlexaff
Luregn J. Schlapbach, Christoph Aebi, Annette G. Hansen, Andreas Hirt, Jens C. Jensenius, Roland A. Ammann

Bibliographic record

VenueClinical & Experimental Immunology · 2009
Typearticle
Languageen
FieldMedicine
TopicNeutropenia and Cancer Infections
Canadian institutionsPediatric Oncology Group
Fundersnot available
KeywordsImmunologyNeutropeniaMedicineFicolinLeukopeniaCancerInnate immune systemImmune systemInternal medicineToxicity

Abstract

fetched live from OpenAlex

H-ficolin (Hakata antigen, ficolin-3) activates the lectin pathway of complement similar to mannose-binding lectin. However, its impact on susceptibility to infection is currently unknown. This study investigated whether the serum concentration of H-ficolin at diagnosis is associated with fever and neutropenia (FN) in paediatric cancer patients. H-ficolin was measured by time-resolved immunofluorometric assay in serum taken at cancer diagnosis from 94 children treated with chemotherapy. The association of FN episodes with H-ficolin serum concentration was analysed by multivariate Poisson regression. Median concentration of H-ficolin in serum was 26 mg/l (range 6-83). Seven (7%) children had low H-ficolin (< 14 mg/l). During a cumulative chemotherapy exposure time of 82 years, 177 FN episodes were recorded, 35 (20%) of them with bacteraemia. Children with low H-ficolin had a significantly increased risk to develop FN [relative risk (RR) 2.24; 95% confidence interval (CI) 1.38-3.65; P = 0.004], resulting in prolonged duration of hospitalization and of intravenous anti-microbial therapy. Bacteraemia occurred more frequently in children with low H-ficolin (RR 2.82; CI 1.02-7.76; P = 0.045). In conclusion, low concentration of H-ficolin was associated with an increased risk of FN, particularly FN with bacteraemia, in children treated with chemotherapy for cancer. Low H-ficolin thus represents a novel risk factor for chemotherapy-related infections.

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 score0.036
Threshold uncertainty score0.496

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 head0.027
GPT teacher head0.388
Teacher spread0.361 · 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

Citations46
Published2009
Admission routes1
Has abstractyes

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