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EPSTEIN-BARR VIRUS SEROLOGY AND POSTTRANSPLANT LYMPHOPROLIFERATIVE DISEASE IN LUNG TRANSPLANTATION

2001· article· en· W1976276737 on OpenAlexaff
Dennis A. Wigle, Cecilia Chaparro, Atul Humar, Michael Hutcheon, Charles K. Chan, Shaf Keshavjee

Bibliographic record

VenueTransplantation · 2001
Typearticle
Languageen
FieldMedicine
TopicViral-associated cancers and disorders
Canadian institutionsToronto General HospitalUniversity of Toronto
Fundersnot available
KeywordsMedicineLung transplantationTransplantationSerologyInternal medicineIncidence (geometry)ContraindicationCohortLymphoproliferative disordersImmunologyPathologyLymphomaAntibody

Abstract

fetched live from OpenAlex

BACKGROUND: Posttransplant lymphoproliferative disease (PTLD) is now a widely recognized complication of lung transplantation. In the current study, we present our experience with PTLD over a 15-year period, which includes the incidence rates in 242 lung allografts and the relative risk of developing PTLD in 146 patients with known pretransplantation Epstein-Barr virus (EBV) status. METHODS: Inpatient and outpatient charts of 300 consecutive lung transplant recipients between 1984 and 1999 were retrospectively reviewed. RESULTS: Twelve cases of PTLD were observed for a total incidence rate of 5.0%. Ten of these patients had pretransplantation EBV testing, and the consequent increase in relative risk for patients who were EBV negative was 6.8-fold. The mean time between organ transplantation and tissue diagnosis of PTLD was 17.6 months. Total 1-year survival rate from the time of diagnosis for the cohort was 58%, whereas 2-year survival rate was 50%. Median survival for the six patients who died was 4.5 months. CONCLUSIONS: These data suggest that although EBV seronegativity does carry a 6.8-fold increase in the relative risk of developing PTLD, long-term survival despite the development of PTLD can be achieved, and thus EBV seronegativity by itself should not be considered a contraindication to lung transplantation.

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 score0.042
Threshold uncertainty score0.821

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 head0.011
GPT teacher head0.265
Teacher spread0.254 · 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

Citations45
Published2001
Admission routes1
Has abstractyes

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