Impact of Conditioning Regimen on Outcomes for Patients with Lymphoma Undergoing High-Dose Therapy with Autologous Hematopoietic Cell Transplantation
Why this work is in the frame
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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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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 it