Long‐term follow‐up demonstrates curative potential of autologous stem cell transplantation for relapsed follicular lymphoma
Bibliographic record
Abstract
Although autologous stem cell transplantation (ASCT) can achieve durable responses in eligible patients with follicular lymphoma (FL), long-term follow-up is needed to determine if it has curative potential. This retrospective, multicenter study included 162 patients who received ASCT for relapsed FL in Alberta, Canada. With a median (range) follow-up time of 12.5 years (0.1-27.9), the 12-year time-to-progression (TTP) was 57% (95% confidence interval [CI] 49%-65%), time-to-next-treatment was 61% (95% CI 52%-69%), progression-free survival was 51% (95% CI 42%-59%) and overall survival was 69% (95% CI 60%-76%). A plateau emerged on the TTP curve at 57% starting 9 years after ASCT with no relapses occurring beyond this timepoint. Ten patients remained in remission 20 years or more after ASCT. Patients undergoing ASCT at first or second relapse had superior outcomes compared to third or later relapse (12-year TTP 61% vs. 34%), as did patients without progression of disease within 24 months (POD24) of frontline treatment versus those with POD24 (12-year TTP 67% vs. 50%). ASCT achieves high rates of durable remission in relapsed FL, with long-term follow-up revealing that more than 50% of transplanted patients may be functionally cured of their lymphoma. The optimal timing to consider ASCT is at first or second relapse, regardless of POD24 status.
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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 | 0.000 | 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".