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Record W2889467068 · doi:10.1016/j.ebiom.2018.08.045

Association of blood IgG with tumor necrosis factor-alpha and clinical course of chronic lymphocytic leukemia

2018· article· en· W2889467068 on OpenAlexafffund
David Spaner, Robertson Venema, Justin S. Huang, Peter A. A. Norris, Alan H. Lazarus, Guizhi Wang, Yonghong Shi

Bibliographic record

VenueEBioMedicine · 2018
Typearticle
Languageen
FieldMedicine
TopicChronic Lymphocytic Leukemia Research
Canadian institutionsSt. Michael's HospitalUniversity of TorontoSunnybrook Health Science Centre
FundersCanadian Institutes of Health ResearchLeukemia and Lymphoma Society of CanadaCSL Behring
KeywordsMedicineChronic lymphocytic leukemiaImmunologyImmunoglobulin GAntibodyImmune systemImmunotherapyTumor necrosis factor alphaInternal medicineLeukemia

Abstract

fetched live from OpenAlex

The intrinsic humoral immunodeficiency of chronic lymphocytic leukemia (CLL) is often managed with immunoglobulin replacement therapy (IgRT) to maintain IgG levels in the low-normal range (6-8 g/L) but optimal targets for IgG and timing to commence IgRT are unclear. IgG levels fell near 6 g/L at rates of -0.85±0.14 g/L/year in 51 patients who required treatment for CLL within 4.5±0.4 years from initial diagnosis and - 0.27±0.04 g/L/year in 40 patients with progressive disease who remained untreated after 8.5±0.5 years. In contrast, endogenous IgG levels remained above 8 g/L in patients with highly indolent disease (n = 25) and TNFα and beta-2-microglobulin (β2M) in blood decreased when IgRT was used to increase IgG levels over 9 g/L. At 15 g/L but not 5 g/L, the IgRT product Hizentra® inhibited B cell receptor (BCR)-activation, TNFα production, and survival in vitro, particularly of CLL cells that spontaneously made little TNFα. These findings suggest deterioration of the humoral immune system is associated with progressive CLL and altering the dosing of IgRT to achieve higher than conventional IgG target levels may have therapeutic activity.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.306
Threshold uncertainty score0.981

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.016
GPT teacher head0.330
Teacher spread0.314 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations15
Published2018
Admission routes2
Has abstractyes

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