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Record W2278702400 · doi:10.1136/bcr-2015-212103

The first report of a previously undescribed EBV-negative NK-cell lymphoma of the GI tract presenting as chronic diarrhoea with eosinophilia

2015· article· en· W2278702400 on OpenAlexaff
Ahmad Zaheen, Jan Delabie, Rajkumar Vajpeyi, David Frost

Bibliographic record

VenueBMJ Case Reports · 2015
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmune Cell Function and Interaction
Canadian institutionsUniversity Health NetworkUniversity of Toronto
Fundersnot available
KeywordsMedicineEosinophiliaLymphomaPathologyAbdominal painImmunophenotypingDifferential diagnosisBone marrowGastroenterologyImmunologyInternal medicine

Abstract

fetched live from OpenAlex

A 74-year-old man presented with a 2-month history of watery diarrhoea. His complete blood count showed lymphopaenia and marked eosinophilia. Investigations for common infectious causes including Clostridium difficile toxin, stool culture, ova and parasites were negative. Endoscopy revealed extensive colitis and a CT of the abdomen identified numerous large abdominal lymph nodes suspicious for lymphoma. Multiple tissue samples were obtained; colon, mesenteric lymph node and bone marrow biopsy, as well as pleural fluid from a rapidly developing effusion, confirmed the presence of metastatic lymphoma with an immunophenotype most consistent with an aggressive variant of Epstein-Barr virus (EBV)-negative natural killer (NK)-cell lymphoma. The patient's clinical condition rapidly deteriorated and he died shortly following diagnosis. To the best of our knowledge, this is the first case report of a primary gastrointestinal EBV-negative NK-cell lymphoma, and its clinical presentation highlights the importance of a broad differential in the management of chronic diarrhoea.

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)0.000
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0020.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0050.002
Insufficient payload (model declined to judge)0.0030.002

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 head0.017
GPT teacher head0.257
Teacher spread0.240 · 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 designCase report
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

Citations4
Published2015
Admission routes1
Has abstractyes

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