Timing of high-dose methotrexate CNS prophylaxis in DLBCL: a multicenter international analysis of 1384 patients
Bibliographic record
Abstract
Prophylactic high-dose methotrexate (HD-MTX) is often used for diffuse large B-cell lymphoma (DLBCL) patients at high risk of central nervous system (CNS) relapse, despite limited evidence demonstrating efficacy or the optimal delivery method. We conducted a retrospective, international analysis of 1384 patients receiving HD-MTX CNS prophylaxis either intercalated (i-HD-MTX) (n = 749) or at the end (n = 635) of R-CHOP/R-CHOP-like therapy (EOT). There were 78 CNS relapses (3-year rate 5.7%), with no difference between i-HD-MTX and EOT: 5.7% vs 5.8%, P = .98; 3-year difference: 0.04% (-2.0% to 3.1%). Conclusions were unchanged on adjusting for baseline prognostic factors or on 6-month landmark analysis (n = 1253). In patients with a high CNS international prognostic index (n = 600), the 3-year CNS relapse rate was 9.1%, with no difference between i-HD-MTX and EOT. On multivariable analysis, increasing age and renal/adrenal involvement were the only independent risk factors for CNS relapse. Concurrent intrathecal prophylaxis was not associated with a reduction in CNS relapse. R-CHOP delays of ≥7 days were significantly increased with i-HD-MTX vs EOT, with 308 of 1573 (19.6%) i-HD-MTX treatments resulting in a delay to subsequent R-CHOP (median 8 days). Increased risk of delay occurred in older patients when delivery was later than day 10 in the R-CHOP cycle. In summary, we found no evidence that EOT delivery increases CNS relapse risk vs i-HD-MTX. Findings in high-risk subgroups were unchanged. Rates of CNS relapse in this HD-MTX-treated cohort were similar to comparable cohorts receiving infrequent CNS prophylaxis. If HD-MTX is still considered for certain high-risk patients, delivery could be deferred until R-CHOP completion.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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".