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Survival of Burkholderia cepacia sepsis following lung transplantation in recipients with cystic fibrosis

2010· review· en· W1511421728 on OpenAlexaff
E.F. Nash, Aman S. Coonar, Richard Kremer, Elizabeth Tullis, Michael Hutcheon, L.G. Singer, Shaf Keshavjee, Cecilia Chaparro

Bibliographic record

VenueTransplant Infectious Disease · 2010
Typereview
Languageen
FieldMedicine
TopicCystic Fibrosis Research Advances
Canadian institutionsUniversity of TorontoUniversity Health NetworkSt. Michael's Hospital
Fundersnot available
KeywordsBurkholderia cenocepaciaMedicineCystic fibrosisSepsisLung transplantationBurkholderia cepacia complexTransplantationBurkholderiaLungInternal medicineBiologyBacteria

Abstract

fetched live from OpenAlex

E.F. Nash, A. Coonar, R. Kremer, E. Tullis, M. Hutcheon, L.G. Singer, S. Keshavjee, C. Chaparro. Survival of Burkholderia cepacia sepsis following lung transplantation in recipients with cystic fibrosis.Transpl Infect Dis 2010: 12: 551–554. All rights reserved Abstract: Cystic fibrosis (CF) lung transplant recipients infected with Burkholderia cenocepacia have a worse survival rate after lung transplantation than those who are not infected with this organism. The decreased survival is predominantly due to recurrent B. cenocepacia infection, with the majority of affected recipients succumbing within 3 months after transplant. B. cepacia complex (BCC) sepsis is one of the defining criteria for cepacia syndrome, an almost universally fatal necrotizing pneumonic illness. We report 2 CF patients who were long-term survivors of B. cenocepacia sepsis after lung transplantation. The aim of this report is to demonstrate that, although survival of B. cenocepacia sepsis after lung transplantation is extremely uncommon, with aggressive multidisciplinary management, long-term survival remains a realistic objective.

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)0.001
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: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.019
GPT teacher head0.327
Teacher spread0.308 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations40
Published2010
Admission routes1
Has abstractyes

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