Consolidative Autotransplantation Achieves High Cure Rates in Adverse-Risk Large B Cell Lymphoma
Bibliographic record
Abstract
There remains an unmet need to optimize the first-line treatment of patients with high-risk large B cell lymphoma (LBCL), particularly those with a high International Prognostic Index (IPI) score or a positive interim positron emission tomography (PET) scan who experience poor outcomes with R-CHOP. This study was conducted to evaluate the real-world effectiveness of consolidative autologous stem cell transplantation (ASCT) among patients with high-risk LBCL. This retrospective study included consecutive patients with LBCL and IPI score 4 or 5 who underwent consolidative ASCT as part of first-line therapy in Alberta, Canada. Progression-free survival (PFS), overall survival (OS), and disease-specific survival (DSS) were determined using the Kaplan-Meier method. The study cohort comprised 114 patients with median age of 60 years (range, 18 to 73 years), of whom 81 (71%) had an IPI score of 4 and 33 (29%) had an IPI score of 5. With a median follow-up of 5.6 years, the 5-year PFS was 72% (95% confidence interval [CI], 62% to 79%), 5-year OS was 74% (95% CI, 64% to 81%), and 5-year DSS was 80% (95% CI, 71% to 87%). There was no significant difference in PFS among patients with and patients without positive interim PET scans (n = 24), MYC and BCL2 and/or BCL6 rearrangements (n = 26), or central nervous system involvement (n = 15). Consolidative ASCT is associated with high cure rates and favorable survival outcomes in patients with high-risk LBCL and may overcome the adverse prognostic impact of a positive interim PET scan.
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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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".