Obinutuzumab for the treatment of patients with previously untreated chronic lymphocytic leukemia: overview and perspective
Bibliographic record
Abstract
Chronic lymphocytic leukemia (CLL) is the most common lymphoproliferative disorder in the Western world and predominantly affects older people. Until recently, most studies in CLL focused on younger patients in whom intensive therapy with the addition of rituximab to fludarabine and cyclophosphamide was shown to improve survival. Obinutuzumab is a novel type II anti-CD20 monoclonal antibody (mAb) that recently demonstrated an overall survival advantage when combined with chemotherapy in previously untreated older patients with CLL and comorbidities. Obinutuzumab was superior to rituximab in this same study in terms of response rates and progression-free survival. Several preclinical and early phase clinical studies also support the efficacy of obinutuzumab. The most frequent adverse event noted with obinutuzumab is infusion-related reactions, which occur more frequently than with rituximab and are typically restricted to the first cycle of therapy. Based on these results, obinutuzumab should be considered the gold standard mAb for combination with chemotherapy in previously untreated patients with CLL and comorbidities. The marked efficacy of obinutuzumab with a weak chemotherapy backbone implies significant potency of this mAb, making it the ideal partner for combination studies with other agents in CLL.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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".