Autologous and Allogeneic Stem-Cell Transplantation for Transformed Follicular Lymphoma: A Report of the Canadian Blood and Marrow Transplant Group
Bibliographic record
Abstract
PURPOSE: To determine whether autologous (auto) or allogeneic (allo) stem-cell transplantation (SCT) improves outcome in patients with transformed follicular lymphoma compared with rituximab-containing chemotherapy alone. PATIENTS AND METHODS: This was a multicenter cohort study of patients with follicular lymphoma and subsequent biopsy-proven aggressive histology transformation. Patient, treatment, and outcome data were collected from each transplantation center and combined for analysis. A separate control group was composed of patients with transformation treated with rituximab-containing chemotherapy but not SCT. The primary end point was overall survival (OS) after transformation. RESULTS: One hundred seventy-two patients were identified: 22 (13%) treated with alloSCT, 97 (56%) with autoSCT, and 53 (31%) with rituximab-containing chemotherapy. Five-year OS after transformation was 46% for patients treated with alloSCT, 65% with autoSCT, and 61% with rituximab-containing chemotherapy (P = .24). Five-year progression-free survival (PFS) after transformation was 46% for those treated with alloSCT, 55% with autoSCT, and 40% with rituximab-containing chemotherapy (P = .12). In multivariate analysis, patients treated with autoSCT had improved OS compared with those who received rituximab-containing chemotherapy (hazard ratio [HR], 0.13; 95% CI, 0.05 to 0.34; P < .001). On the other hand, there was no OS difference between those treated with alloSCT and rituximab-containing chemotherapy (HR, 0.44; 95% CI, 0.16 to 1.24; P = .12). OS and PFS after SCT were similar between those treated with autoSCT and alloSCT. Five-year transplantation-related mortality was 23% for those treated with alloSCT and 5% for autoSCT. CONCLUSION: Patients undergoing autoSCT had better outcomes than those treated with rituximab-containing chemotherapy alone. AlloSCT did not improve outcome compared with rituximab-containing chemotherapy and was associated with clinically significant 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".