Impact of rituximab on treatment outcomes of patients with diffuse large b‐cell lymphoma: a population‐based analysis
Bibliographic record
Abstract
We conducted a multi-institutional population-based analysis of the survival and toxicity associated with the addition of rituximab to chemotherapy for patients with diffuse large B-cell lymphoma (DLBCL), including patients aged ≥ 80 years, who were excluded from published randomized trials. Using population-based registries in Ontario, we identified 4021 patients who received chemotherapy with or without rituximab (R-CHOP [rituximab with cyclophosphamide, doxorubicin, vincristine and prednisone] or CHOP) for DLBCL between 1996 and 2007, including 397 patients aged ≥ 80 years. After propensity score matching, the overall survival (OS) and significant toxicities for R-CHOP and CHOP treatment groups were compared. R-CHOP was associated with a significant increase in 5-year OS compared to CHOP alone (62% vs. 54%; hazard of death = 0·78, P = 0·0004). Survival benefit was seen in all age groups, including those aged ≥ 80 years. Patients treated with rituximab did not have a significant increase in 1-year hospitalization rates for cardiac, pulmonary, gastrointestinal or neurological diagnoses compared to those treated with CHOP alone. The addition of rituximab to CHOP improves survival in the general population of patients with DLBCL and produces early survival benefit for very elderly patients, without any significant increase in the risk of serious toxicity.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| 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 | 0.001 | 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".