Tandem Autologous–Allogeneic Nonmyeloablative Sibling Transplantation in Relapsed Follicular Lymphoma Leads to Impressive Progression-Free Survival with Minimal Toxicity
Bibliographic record
Abstract
Autologous stem cell transplantation (ASCT) prolongs survival in patients with relapsed follicular lymphoma. ASCT is usually not curative, however. Myeloablative allogeneic transplantation has produced long-term survival at a cost of significant transplantation-related mortality (TRM), whereas reduced-intensity transplantation entails less TRM but has a higher relapse rate. We thus initiated a protocol consisting of ASCT followed by nonmyeloablative allogeneic transplantation (NMT) for relapsed follicular lymphoma to mimic myeloablative allogeneic transplantation without the associated toxicity. The NMT was non-T cell-depleted, and all donors were HLA-identical siblings. We report results in 27 patients with a median age of 49 years (range, 34-65 years). Five patients demonstrated histological progression toward an aggressive lymphoma. The patients had received a median of 3 lines of previous therapy. Disease status before ASCT included 8 patients in complete remission, 14 in partial remission, and 5 refractory. Five patients developed grade II-IV acute graft-versus-host disease, and 20 patients developed chronic graft-versus-host disease requiring systemic therapy. With a median follow-up of 39 months after NMT, overall survival and progression-free survival were 96% at 3 years. We conclude that the combined ASCT-NMT strategy appears to be safe, with excellent progression-free survival even in refractory and transformed cases. This novel approach warrants further investigation in larger prospective studies.
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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 | 0.000 |
| 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) | 0.000 | 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".