MétaCan
Menu
Back to cohort
Record W2763948152

Use of Obinutuzumab for B-cell Malignancies

2017· article· en· W2763948152 on OpenAlexaff
Mita Manna, Carolyn Owen

Bibliographic record

VenueMedical Research Archives · 2017
Typearticle
Languageen
FieldMedicine
TopicChronic Lymphocytic Leukemia Research
Canadian institutionsFoothills Medical CentreUniversity of Calgary
Fundersnot available
KeywordsObinutuzumabRituximabCD20MedicineChronic lymphocytic leukemiaMonoclonal antibodyOncologyMonoclonal antibody therapyLymphomaMonoclonalInternal medicineImmunologyLeukemiaAntibody
DOInot available

Abstract

fetched live from OpenAlex

We analysed data for the use of obinutuzumab in the treatment of CD20-positive lymphoproliferative disorders including chronic lymphocytic leukemia (CLL) and non-Hodgkin lymphomas (NHL). Marked progress in the outcomes of B-cell NHL came with the development of targeted therapy against CD20 with the monoclonal antibody rituximab. Despite the benefit seen with rituximab, many patients relapse or become refractory after rituximab-containing therapies. This led to the development of more effective anti-CD20 monoclonal antibodies such as obinutuzumab. Several Phase III studies have been conducted comparing rituximab to obinutuzumab in patients with B-cell NHL. Obinutuzumab is a glycoengineered Type II anti-CD20 monoclonal antibody. An overview of the recently presented and/or published Phase III studies investigating obinutuzumab in the treatment of NHL and CLL are presented. The CLL11 Phase III study was the first study demonstrating the superiority of obinutuzumab over rituximab. Recently, several other Phase III studies have demonstrated improved outcomes for CLL and NHL with the use of obinutuzumab. Further evaluation, longer follow-up, and future studies investigating combination therapy with novel agents are warranted to demonstrate if obinutuzumab should replace rituximab as the standard of care.

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)0.002
metaresearch head score (Gemma)0.034
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.664
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.034
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.004
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.196
GPT teacher head0.448
Teacher spread0.251 · 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
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

Citations0
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueMedical Research ArchivesSame topicChronic Lymphocytic Leukemia ResearchFrench-language works237,207