Favorable Outcomes with R-CHOP Induction and Consolidative Autologous Stem Cell Transplantation for Double-Hit Lymphoma
Bibliographic record
Abstract
Double-hit lymphoma (DHL) is an aggressive large B cell lymphoma associated with a poor prognosis with R-CHOP chemotherapy. The optimal treatment is unknown, but outcomes might be improved with intensive induction regimens or consolidative high-dose chemotherapy and autologous stem cell transplantation (HDT/ASCT). The purpose of this study was to determine the real-world outcomes of patients with DHL treated with primarily R-CHOP induction and consolidative HDT/ASCT. This retrospective, multicenter study included consecutive patients age 18 to 70 years with newly diagnosed DHL intended for consolidative HDT/ASCT in Alberta, Canada. Progression-free survival (PFS) and overall survival (OS) were determined using the Kaplan-Meier method. The cohort comprised 58 patients with a median age of 59.5 years (range, 30 to 69 years). High-risk features at diagnosis included International Prognostic Index score 3 to 5 in 45 patients (78%), transformed indolent lymphoma in 25 (43%), and central nervous system involvement in 3 (5%). Forty-six patients (79%) patients received R-CHOP induction, and 45 (78%) proceeded to consolidative HDT/ASCT. With a median follow-up of 4.6 years, the 4-year PFS and OS rates were 67% (95% confidence interval [CI], 53% to 78%) and 68% (95% CI, 54% to 79%), respectively, for all patients and 86% (95% CI, 72% to 93%) and 88% (95% CI, 73% to 95%) for those undergoing HDT/ASCT. R-CHOP induction and consolidative HDT/ASCT result in excellent outcomes for patients with chemosensitive DHL, whereas patients with primary refractory disease might benefit from alternative strategies, such as earlier use of chimeric antigen receptor T cell therapy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 teacher head, 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".