High dose salvage therapy with dose intensive cyclophosphamide, etoposide and cisplatin may increase transplant rates for relapsed/refractory aggressive non-Hodgkin lymphoma
Bibliographic record
Abstract
Only one-quarter to one-third of patients with relapsed/refractory aggressive non-Hodgkin lymphoma (r/r-aNHL) treated with common salvage chemotherapy regimens and autologous stem cell transplant (ASCT) achieve 5-year progression-free survival (PFS). Worse outcomes have been reported after failure of prior rituximab-containing induction, initial time to progression (TTP) < 1 year or age-adjusted International Prognostic Index (aaIPI) = 2-3 at relapse. In Calgary, we have treated patients with r/r-aNHL with dose-intensive cyclophosphamide 5.25 g/m(2), etoposide 1.05 g/m(2) and cisplatin 105 mg/m(2) (DICEP) for both re-induction therapy and autologous blood stem cell mobilization. In this study we retrospectively analyzed 113 consecutive transplant-eligible patients with r/r-aNHL who received one cycle of DICEP (n = 93) or R-DICEP (n = 20) from 1995 to 2009. Patient characteristics included: median age = 49 years (22-69); TTP < 1 year = 85; elevated lactate dehydrogenase (LDH) = 60; Eastern Cooperative Oncology Group performance status (ECOG) 2-4 = 42; aaIPI 2-3 = 59; bulk > 10 cm = 26, prior rituximab = 27. The median number of CD34 + cells collected was 19 × 10(6)/kg (0.3-142), 83.5% responded and 90% (102) proceeded to ASCT. The 5-year PFS rate was 42% for all patients, 32% for those with relapse aaIPI = 2-3, 35% for initial TTP < 1 year and 56% for those who failed initial rituximab induction. In conclusion, (R)DICEP is an effective re-induction regimen for r/r-aNHL, leading to excellent stem cell mobilization, a high chance of proceeding to ASCT and encouraging long-term PFS rates.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".