Durable event-free survival following autologous stem cell transplant for relapsed or refractory follicular lymphoma: positive impact of recent rituximab exposure and low-risk Follicular Lymphoma International Prognostic Index score
Bibliographic record
Abstract
Published studies have provided conflicting results regarding the curative potential of high dose chemotherapy and autologous stem cell transplant (HDT/ASCT) for follicular lymphoma (FL). Our objectives were to evaluate the long-term event-free (EFS) and overall (OS) survival rates following ASCT for FL, and to identify predictors of improved outcome. We conducted a retrospective analysis of the first 100 consecutive patients with relapsed or refractory FL treated with HDT/ASCT in Calgary from 1993 to 2008. With a median follow-up of 65 months (range 16-178) post-ASCT, 5-year EFS and OS rates were 56% (95% confidence interval [CI] 46-66%) and 70% (95% CI 61-79%), respectively. A plateau on the EFS curve is evident starting 6 years post-ASCT. Also, the EFS post-ASCT was markedly longer than the 12-month median EFS from last therapy prior to ASCT (p < 0.0001). Failure of rituximab pre-ASCT was not associated with EFS or OS. Severe toxicities included two early treatment-related deaths, and four late deaths from secondary leukemia. Independent predictors of EFS and OS in multivariate analysis were rituximab therapy within 6 months of ASCT, chemosensitivity and FLIPI (FL International Prognostic Index) score 0-1. In conclusion, our data suggest that over 50% of patients with relapsed/refractory FL who have failed 1-2 prior chemotherapy regimens achieve long-term EFS following HDT/ASCT.
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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".