Diffuse large B‐cell lymphoma with testicular involvement: outcome and risk of <scp>CNS</scp> relapse in the rituximab era
Bibliographic record
Abstract
The addition of rituximab has improved outcomes in diffuse large B-cell lymphoma (DLBCL), however, there remains limited information on the impact of rituximab in those with testicular involvement. All patients with diffuse large cell lymphoma and testicular involvement treated with curative intent were identified in the British Columbia Cancer Agency Lymphoid Cancer Database. In total, 134 patients diagnosed between 1982 and 2015 with diffuse large cell lymphoma involving the testis were identified: 61 received CHOP (cyclophosphamide, doxorubicin, vincristine, prednisone)-like chemotherapy and 73 received CHOP plus rituximab (R-CHOP). A greater proportion of R-CHOP treated patients had higher International Prognostic Index (IPI, P = 0·005). In multivariate analysis, the protective effect of rituximab on progression-free survival (hazard ratio (HR) 0·42, P < 0·001), overall survival (HR 0·39, P < 0·001) and cumulative incidence of progression (HR 0·46, P = 0·014) were independent of the IPI. However, in a competing risk multivariate analysis including central nervous system (CNS) prophylaxis and the CNS-IPI, rituximab was not associated with a decreased risk of CNS relapse. The addition of rituximab has reduced the risk of lymphoma recurrence in testicular DLBCL, presumably through improved eradication of systemic disease. However, CNS relapse risk remains high and further studies evaluating effective prophylactic strategies are needed.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".