Improved survival outcomes with the addition of rituximab to initial therapy for chronic lymphocytic leukemia: a comparative effectiveness analysis in the province of British Columbia, Canada
Bibliographic record
Abstract
Chemoimmunotherapy with rituximab improves survival in clinical trials in upfront chronic lymphocytic leukemia (CLL) treatment. This study compared clinical outcomes with and without rituximab added to first-line chemotherapy in a provincial cohort of CLL patients. Between 1973 and 2014, 1345 patients received CLL treatment: 48% with rituximab, 52% chemotherapy alone. Median overall survival (OS) and treatment-free survival (TFS) were significantly longer with rituximab: OS 8.9 vs. 6.2 years, p < .0001; TFS 3.6 vs. 2.1 years, p < .0001. Addition of rituximab to chemotherapy was a strong independent predictor of mortality with a 32% mortality reduction after controlling for co-variates (age, sex, stage, and treatment with purine analogs). This large population-based study complements clinical trial and registry data demonstrating the benefit of adding rituximab to first-line CLL therapy and adds further evidence of the efficacy of rituximab-based chemoimmunotherapy in a real-world setting.
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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".