Does Upfront Autologous Stem Cell Transplant at First Relapse Improve Outcome in Transplant Eligible Follicular Lymphoma Patients Who Relapse within 24 Months
Bibliographic record
Abstract
In Canadian adults, Follicular lymphoma (FL) is the most common subtype of NHL, Approximately 20% of patients with FL experience progression of disease (POD) within 2 years of first line chemoimmunotherapy. Those patients have an expected overall survival of less than 5 years.The optimal second-line treatment for these high-risk patients is unclear. We analyzed data from the blood and bone marrow transplantation Center at the Ottawa Hospital (TOH) to determine whether autologous hematopoietic cell transplant (ASCT) as upfront therapy for first relapse can improve outcomes in this high-risk FL subgroup. We identified 17 patients who underwent upfront ASCT between February 2012 and February 2019. All of them had relapsed within 24 months after their 1st Rituximab-based chemotherapy. The OS at 2-year and 5-year was 86.2% (95% CI: 55-96) and 71.8% (95% CI: 31-91) respectively. The PFS at 2 year and 5 year was 62.6% (95% CI: 35-81) and 53.6% (95% CI: 25-75) respectively. We demonstrate improved OS when receiving autologous hematopoietic stem cell transplant as up front at first relapse in transplant eligible follicular lymphoma who relapse within 24 months of first line therapy. However; the sample size considerably small but the results look promising. Combining other center experience the confidence intervals are wide, indicating that the sample size was too small. Considering a single center, the result looks promising, but the data need to be replicated with a larger sample size. Disclosures MacDonald: Roche Canada: Consultancy, Honoraria; Janssen: Honoraria; AstraZeneca: Honoraria.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".