Autologous Stem Cell Transplantation in Follicular Lymphoma: a Systematic Review and Meta-analysis
Bibliographic record
Abstract
BACKGROUND: The impact of high-dose therapy and autologous stem cell transplantation (ASCT) vs conventional-dose chemotherapy in the initial management of adults with advanced follicular lymphoma (FL) on overall survival remains uncertain. We performed a systematic review of the randomized clinical trials addressing this question. METHODS: We searched MEDLINE, EMBASE, CENTRAL, American Society of Hematology, American Society of Clinical Oncology, BIOSIS, PAPERSFIRST, PROCEEDINGS, clinical trials registries, and bibliographies of relevant studies for randomized clinical trials comparing myeloablative chemotherapy with ASCT to any chemotherapy in adults with untreated advanced FL. We performed a meta-analysis using random effects models to estimate overall survival, event-free survival, and risks of adverse outcomes. Statistical heterogeneity was calculated by using the I(2) statistic. RESULTS: Seven trials proved eligible, four of which provided data from 941 patients that could be included in a meta-analysis and three of which remain unpublished. In two of the trials, patients in both arms received rituximab during the induction treatment. Moderate quality evidence from the three trials that reported overall survival (n = 701 patients) suggests that ASCT did not result in improved overall survival (hazard ratio of death = 0.99, 95% confidence interval [CI] = 0.73 to 1.33). Low-quality evidence from the four trials of 941 patients suggests improvement in event-free survival in favor of ASCT (hazard ratio of death = 0.54, 95% CI = 0.36 to 0.82) with substantial heterogeneity (I(2) = 80%). Adverse outcomes of treatment-related mortality, myelodysplastic syndrome, acute myeloid leukemia, and solid tumors were not different between the two arms (relative risk [RR] of treatment-related mortality = 1.04, 95% CI = 0.29 to 3.70; RR of myelodysplastic syndrome/acute myeloid leukemia = 2.19, 95% CI = 0.45 to 10.55; I(2) = 48%; and RR of solid tumors = 1.30, 95% CI = 0.33 to 5.08). The absolute risk of death from treatment was 14 per 1000 patients for those who received chemotherapy and 15 per 1000 for those who received ASCT (range = 4-52). CONCLUSIONS: Available evidence suggests that high-dose therapy and ASCT as part of FL initial treatment does not improve overall survival. Future trials of ASCT in the context of current chemoimmunotherapy approaches to FL are needed.
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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 | 0.013 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.030 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".