A 15-Year Analysis of Early and Late Autologous Hematopoietic Stem Cell Transplant in Relapsed, Aggressive, Transformed, and Nontransformed Follicular Lymphoma
Bibliographic record
Abstract
Autologous stem cell transplant (ASCT) has been shown to be an effective treatment for follicular lymphoma (FL). We explored our experience in ASCT for FL among all patients treated over a 15-year period from diagnosis through their entire treatment history including relapse post ASCT. All patients who underwent an unpurged ASCT for relapsed, advanced FL between June 1990 and December 2000 were analyzed. After salvage therapy they received melphalan/etoposide/total body irradiation, BCNU, etoposide, cytarabine, melphalan (BEAM), or cyclophosphamide BCNU etoposide (CBV) as conditioning for the ASCT. One hundred thirty-eight patients with a median age of 48 years and a median follow-up of 7.6 years were analyzed. The majority were of the subtype grade 1, nontransformed (FL-NT), having had 1 prior chemotherapy. The progression-free (PFS) and overall survival (OS) of the FL-NT at 10 years were 46% and 57%, respectively, and at 5 years for the transformed (FL-T) were 25% and 56%, respectively, of which only the PFS was significantly different (P=.007). The median OS from diagnosis was 16 years for the FL-NT. ASCT positively altered the trend of shorter remissions with subsequent chemotherapies, and there was no difference in OS between those who had 1, 2, or >2 chemotherapies prior to ASCT. Salvage therapy for relapse post ASCT was effective (OS>1 year) in a third of patients. Unpurged ASCT is an effective tool in the treatment of relapsed, aggressive FL-NT and FL-T, is superior to retreatment with standard chemotherapy, is effective at various stages of treatment, is likely to have a beneficial influence on the natural history of this disease, and the disease is amenable to salvage therapy post-ASCT relapse.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".