Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".