Rituximab with high‐dose methotrexate in primary central nervous system lymphoma
Bibliographic record
Abstract
The addition of rituximab (R) to chemotherapy improves outcomes in patients with systemic B-cell non-Hodgkin lymphomas, but the impact in patients with primary central nervous system lymphoma (PCNSL) receiving high-dose methotrexate (HDMTX) is unknown. Patients diagnosed with PCNSL at the British Columbia Cancer Agency (BCCA) between 2000 and 2013 were treated with ≥1 cycle of HDMTX 8 g/m(2) every 2 weeks, to best response or 10 cycles. After 2006, rituximab 375 mg/m(2) was given every 2 weeks with HDMTX for a total of 4 doses. 49 (66%) patients received HDMTX alone and 25 (34%) HDMTX+R, with a median of 5 (range 1-10) HDMTX cycles, and no difference between groups. The median follow-up was 5 years: 8.8 years (range 3.15-13.5 years) HDMTX and 1.9 years (range 0.5-7 years) HDMTX+R. The 5-year PFS was 17%, with no difference between groups (HR: 0.75, 95% CI: 0.41-1.35; P = 0.33). The 5-year OS was 38%, with no difference between the groups OS (HR: 0.73, 95% CI: 0.35-1.52; P = 0.39). In this retrospective study comparing two subgroups of patients treated in different eras, the addition of R to HDMTX did not appear to improve outcomes in PCNSL, possibly consistent with its known poor CNS penetration. It is possible that with a larger sample size, longer follow-up, or different rituximab dosing/schedule, the addition of rituximab may lead to a statistically significant improvement in outcomes. Prospective randomized trials currently in progress will more definitively estimate the impact of the addition of rituximab to HDMTX-based chemotherapy for PCNSL.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".