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The Non-Hodgkin Lymphomas

2017· book· en· W2905334699 on OpenAlexaff
James R. Cerhan, Claire M. Vajdic, John J. Spinelli

Bibliographic record

VenueOxford University Press eBooks · 2017
Typebook
Languageen
FieldMedicine
TopicLymphoma Diagnosis and Treatment
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsLymphomaImmunologyImmunodeficiencyMedicineImmune systemBiology

Abstract

fetched live from OpenAlex

The non-Hodgkin lymphomas (NHL) are a heterogeneous group of over forty lymphoid neoplasms that have undergone a major redefinition over the last twenty-five years, in part due to advances in immunology and genetics as well as implementation of the WHO classification system. NHLs are considered clonal tumors of B-cells, T-cells, or natural killer (NK) cells arrested at various stages of differentiation, regardless of whether they present in the blood (lymphoid leukemia) or lymphoid tissues (lymphoma). In the United States, the age-standardized NHL incidence rate (per 100,000) doubled from 1973 (10.2) to 2004 (21.4) and then stabilized, while five-year relative survival rates improved from 42% in 1973 to 70% in 2004. Established risk factors for NHL or specific NHL subtypes include infectious agents (HTLV-1, HIV, EBV, HHV8, HCV, H. pylori), immune dysregulation (primary immunodeficiency, transplantation, autoimmunity, and immunosuppressive drugs), family history of lymphoma, and common genetic variants identified by genome-wide association studies.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.039
Threshold uncertainty score0.130

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0390.038

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 head0.020
GPT teacher head0.233
Teacher spread0.213 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations13
Published2017
Admission routes1
Has abstractyes

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