Impact of Conditioning Regimen on Outcomes for Patients with Lymphoma Undergoing High-Dose Therapy with Autologous Hematopoietic Cell Transplantation
Bibliographic record
Abstract
There are limited data to guide the choice of high-dose therapy (HDT) regimen before autologous hematopoietic cell transplantation (AHCT) for patients with Hodgkin (HL) and non-Hodgkin lymphoma (NHL). We studied 4917 patients (NHL, n = 3905; HL, n = 1012) who underwent AHCT from 1995 to 2008 using the most common HDT platforms: carmustine (BCNU), etoposide, cytarabine, and melphalan (BEAM) (n = 1730); cyclophosphamide, BCNU, and etoposide (CBV) (n = 1853); busulfan and cyclophosphamide (BuCy) (n = 789); and total body irradiation (TBI)-containing treatment (n = 545). CBV was divided into CBV(high) and CBV(low) based on BCNU dose. We analyzed the impact of regimen on development of idiopathic pulmonary syndrome (IPS), transplantation-related mortality (TRM), and progression-free and overall survival. The 1-year incidence of IPS was 3% to 6% and was highest in recipients of CBV(high) (hazard ratio [HR], 1.9) and TBI (HR, 2.0) compared with BEAM. One-year TRM was 4% to 8%, respectively, and was similar between regimens. Among patients with NHL, there was a significant interaction between histology, HDT regimen, and outcome. Compared with BEAM, CBV(low) (HR, .63) was associated with lower mortality in follicular lymphoma (P < .001), and CBV(high) (HR, 1.44) was associated with higher mortality in diffuse large B cell lymphoma (P = .001). For patients with HL, CBV(high) (HR, 1.54), CBV(low) (HR, 1.53), BuCy (HR, 1.77), and TBI (HR, 3.39) were associated with higher mortality compared with BEAM (P < .001). The impact of specific AHCT regimen on post-transplantation survival is different depending on histology; therefore, further studies are required to define the best regimen for specific diseases.
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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 | 0.003 |
| 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) | 0.001 | 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".