Allotransplantation for Patients Age ≥40 Years with Non-Hodgkin Lymphoma: Encouraging Progression-Free Survival
Bibliographic record
Abstract
Non-Hodgkin lymphoma (NHL) disproportionately affects older patients, who do not often undergo allogeneic hematopoietic cell transplantation (HCT). We analyzed Center for International Blood and Marrow Transplant Research data on 1248 patients age ≥40 years receiving reduced-intensity conditioning (RIC) or nonmyeloablative (NMA) conditioning HCT for aggressive (n = 668) or indolent (n = 580) NHL. Aggressive lymphoma was more frequent in the oldest cohort 49% for age 40 to 54 versus 57% for age 55 to 64 versus 67% for age ≥65; P = .0008). Fewer patients aged ≥65 had previous autografting (26% versus 24% versus 9%; P = .002). Rates of relapse, acute and chronic GVHD, and nonrelapse mortality (NRM) at 1 year post-HCT were similar in the 3 age cohorts (22% [95% confidence interval (CI), 19% to 26%] for age 40 to 54, 27% [95% CI, 23% to 31%] for age 55 to 64, and 34% [95% CI, 24% to 44%] for age ≥65. Progression-free survival (PFS) and overall survival (OS) at 3 years was slightly lower in the older cohorts (OS: 54% [95% CI, 50% to 58%] for age 40 to 54; 40% [95% CI, 36% to 44%] for age 55 to 64, and 39% [95% CI, 28% to 50%] for age ≥65; P < .0001). Multivariate analysis revealed no significant effect of age on the incidence of acute or chronic GVHD or relapse. Age ≥55 years, Karnofsky Performance Status <80, and HLA mismatch adversely affected NRM, PFS, and OS. Disease status at HCT, but not histological subtype, was associated with worse NRM, relapse, PFS, and OS. Even for patients age ≥55 years, OS still approached 40% at 3 years, suggesting that HCT affects long-term remission and remains underused in qualified older patients with NHL.
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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 | 0.000 |
| 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".