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Record W2912598520 · doi:10.1080/10428194.2018.1498490

Improving CD20 antibody therapy: obinutuzumab in lymphoproliferative disorders

2019· review· en· W2912598520 on OpenAlexaff
Anca Prica, Michael Crump

Bibliographic record

VenueLeukemia & lymphoma/Leukemia and lymphoma · 2019
Typereview
Languageen
FieldMedicine
TopicChronic Lymphocytic Leukemia Research
Canadian institutionsPrincess Margaret Cancer CentreUniversity of Toronto
Fundersnot available
KeywordsObinutuzumabRituximabMedicineChronic lymphocytic leukemiaCD20Follicular lymphomaLymphomaImmunologyAntibodyMonoclonal antibodyLymphoproliferative disordersOncologyInternal medicineLeukemia

Abstract

fetched live from OpenAlex

Soon after the anti-CD20 monoclonal antibody rituximab began to change the management of indolent and aggressive B cell lymphomas, development of alternative antibodies - including chemoimmunoconjugates - was undertaken. Among humanized and fully human CD20 antibodies, obinutuzumab has emerged as one antibody that seems to have lived up to the promise of improved efficacy based on in vitro and preclinical experiments. The data available, thus, far establish obinutuzumab's preferred role as the anti-CD20 antibody of choice in chronic lymphocytic leukemia and untreated follicular lymphoma, as well as an important addition to the treatment of rituximab-refractory indolent lymphomas. Additional trials in aggressive lymphoma are required to define the place of this new antibody in the management of patients with curable lymphoma subtypes. There are greater toxicities associated with this treatment, including increased infusion-related reactions and cytopenias, but these are manageable with standard supportive care measures.

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 categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.965
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0030.003
Meta-epidemiology (broad)0.0090.002
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0030.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.033
GPT teacher head0.342
Teacher spread0.308 · 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; both teacher heads agree on what is shown here.

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

Citations15
Published2019
Admission routes1
Has abstractyes

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