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Record W2058438955 · doi:10.1097/rlu.0b013e3181e9fb87

Hodgkin Lymphoma Post-Transplant Lymphoproliferative Disorder Following Pediatric Renal Transplant

2010· article· en· W2058438955 on OpenAlexaff
William Makis, Robert Lisbona, Vilma Derbekyan

Bibliographic record

VenueClinical Nuclear Medicine · 2010
Typearticle
Languageen
FieldMedicine
TopicViral-associated cancers and disorders
Canadian institutionsMcGill UniversityRoyal Victoria Hospital
Fundersnot available
KeywordsMedicinePost-transplant lymphoproliferative disorderLymphomaImmunosuppressionRituximabBiopsyLymphoproliferative disordersRadiologyPathologyInternal medicine

Abstract

fetched live from OpenAlex

Post-transplant lymphoproliferative disorder (PTLD) occurs in 1.2% of pediatric renal transplant patients, and is frequently Epstein-Barr Virus mediated. Hodgkin Lymphoma PTLD is the rarest of the 4 types of PTLDs recognized by the World Health Organization, with an incidence of <4% of all PTLD patients. It has a distinct clinical course and treatment from all other types of PTLD. This is a case of a 16-year-old girl who had a renal transplant in 2000 due to Moya Moya disease. Her first F-18 FDG PET/CT done in 2006 showed mildly FDG-avid mediastinal adenopathy (histologically nonspecific reactive nodes), however in 2009, after presenting with fevers, a repeat PET/CT showed extensive intensely FDG-avid disease. Biopsy of a supraclavicular node identified Hodgkin Lymphoma PTLD. The patient was treated with chemotherapy and reimaged, showing excellent response to therapy. In contrast, classic PTLD is treated by withdrawal of immunosuppression and administration of Rituximab. F-18 FDG PET/CT is known to be very useful in the staging and monitoring of response to therapy in the setting of classic PTLD. In this case, serial F-18 FDG PET/CT scans proved very useful in the evaluation and follow-up of the rare and distinct Hodgkin Lymphoma PTLD subtype.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.062
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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

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 head0.016
GPT teacher head0.310
Teacher spread0.294 · 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

Citations8
Published2010
Admission routes1
Has abstractyes

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