Impact of induction regimen and stem cell transplantation on outcomes in double-hit lymphoma: a multicenter retrospective analysis
Bibliographic record
Abstract
Patients with double-hit lymphoma (DHL), which is characterized by rearrangements of MYC and either BCL2 or BCL6, face poor prognoses. We conducted a retrospective multicenter study of the impact of baseline clinical factors, induction therapy, and stem cell transplant (SCT) on the outcomes of 311 patients with previously untreated DHL. At median follow-up of 23 months, the median progression-free survival (PFS) and overall survival (OS) rates among all patients were 10.9 and 21.9 months, respectively. Forty percent of patients remain disease-free and 49% remain alive at 2 years. Intensive induction was associated with improved PFS, but not OS, and SCT was not associated with improved OS among patients achieving first complete remission (P = .14). By multivariate analysis, advanced stage, central nervous system involvement, leukocytosis, and LDH >3 times the upper limit of normal were associated with higher risk of death. Correcting for these, intensive induction was associated with improved OS. We developed a novel risk score for DHL, which divides patients into high-, intermediate-, and low-risk groups. In conclusion, a subset of DHL patients may be cured, and some patients may benefit from intensive induction. Further investigations into the roles of SCT and novel agents 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".