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Record W2135742799 · doi:10.1002/cncr.21586

Biomarkers of potential prognostic significance in diffuse large B‐cell lymphoma

2005· review· en· W2135742799 on OpenAlexaff
Grace Wu, Armand Keating

Bibliographic record

VenueCancer · 2005
Typereview
Languageen
FieldMedicine
TopicLymphoma Diagnosis and Treatment
Canadian institutionsPrincess Margaret Cancer CentreUniversity of TorontoOntario Institute for Cancer Research
Fundersnot available
KeywordsMedicineDiseaseLymphomaDiffuse large B-cell lymphomaCancerBioinformaticsOncologyGene expression profilingComputational biologyInternal medicineGene expressionGeneBiology

Abstract

fetched live from OpenAlex

Diffuse large B-cell lymphoma (DLBCL) is a biologically heterogeneous disease for which the current approach to treatment is only successful for 50% of patients. The prognostic value of various clinical and biological factors in predicting treatment outcome is discussed in this paper. A review of the English literature was performed including original articles and relevant reviews from MEDLINE that addressed the topics of DLBCL biology and potential prognostic factors. The International Prognostic Index is, to date, the most successful clinical model for predicting outcome. In addition, a rapidly expanding list of molecules has been identified by conventional and newer diagnostic methods that may be of significant prognostic value. Gene expression profiling has led to the discovery of new biological subtypes of DLBCL based on patterns of gene expression, and a host of new genes that may play important roles in this disease. Various derangements in apoptosis, cell-cycle regulation, differentiation, and signal transduction have been noted, while the host environment and immune response also appear to modify clinical outcome. Although to our knowledge, the fundamental abnormalities underlying DLBCL remain elusive, progress is being continuously made to further the understanding of the biological heterogeneity of this disease and the use of various clinical and biological variables to predict treatment outcome. The goal is to be able to identify subgroups of patients at high risk of treatment failure and develop more effective treatment based on specific biological defects that may represent new rational therapeutic targets.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.978
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
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 head0.029
GPT teacher head0.330
Teacher spread0.302 · 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 designOther design
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

Citations24
Published2005
Admission routes1
Has abstractyes

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