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Record W4224223806 · doi:10.3390/curroncol29050237

Intravascular Large B-Cell Lymphoma: A Review with a Focus on the Prognostic Value of Skin Involvement

2022· review· en· W4224223806 on OpenAlexvenueno aff
Thomas Breakell, Heidi Waibel, Stefan Schliep, Barbara Ferstl, Michael Erdmann, Carola Berking, Markus V. Heppt

Bibliographic record

VenueCurrent Oncology · 2022
Typereview
Languageen
FieldMedicine
TopicCNS Lymphoma Diagnosis and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineIntravascular large B-cell lymphomaLymphomaBone marrowRituximabBiopsyRadiologyPathologyPresentation (obstetrics)Positron emission tomographyChemotherapyInternal medicine

Abstract

fetched live from OpenAlex

Intravascular large B-cell lymphoma (IVLBCL) is an aggressive Non-Hodgkin lymphoma (NHL) characterised by the presence of neoplastic lymphoid cells within small- and medium-sized blood vessels. According to the clinical presentation, the current WHO classification distinguishes the 'classic' (formerly 'Western') from a hemophagocytic syndrome-associated (formerly 'Asian') variant. A third 'cutaneous' variant has been proposed, characterised by a good prognosis and unique clinical features. While laboratory findings can hint at diagnosis, symptoms are rather nonspecific, and deep skin biopsy supported by further measures such as bone marrow aspiration and positron emission tomography-computed tomography scanning is needed to make a definite diagnosis. Treatment is comprised of anthracycline-based chemotherapy supplemented with rituximab and central nervous system prophylaxis. While there are various prognostic models for NHL, only one is specific to IVLBCL, which does not sufficiently represent some patient groups, especially regarding the lack of differentiation within the patient collective with skin involvement. This underlines the necessity for the establishment of further prognostic models in particular for IVLBCL patients with cutaneous manifestations.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.915
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.103
GPT teacher head0.385
Teacher spread0.282 · 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 designNot applicable
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

Citations28
Published2022
Admission routes1
Has abstractyes

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