Long‐term outcomes among 2‐year survivors of autologous hematopoietic cell transplantation for Hodgkin and diffuse large b‐cell lymphoma
Bibliographic record
Abstract
BACKGROUND: Autologous hematopoietic cell transplantation (auto-HCT) is a standard therapy for relapsed classic Hodgkin lymphoma (cHL) and diffuse large B-cell lymphoma (DLBCL); however, long-term outcomes are not well described. METHODS: This study analyzed survival, nonrelapse mortality, late effects, and subsequent malignant neoplasms (SMNs) in 1617 patients who survived progression-free for ≥2 years after auto-HCT for cHL or DLBCL between 1990 and 2008. The median age at auto-HCT was 40 years; the median follow-up was 10.6 years. RESULTS: The 5-year overall survival rate was 90% (95% confidence interval [CI], 87%-92%) for patients with cHL and 89% (95% CI, 87%-91%) for patients with DLBCL. The risk of late mortality in comparison with the general population was 9.6-fold higher for patients with cHL (standardized mortality ratio [SMR], 9.6) and 3.4-fold higher for patients with DLBCL (SMR, 3.4). Relapse accounted for 44% of late deaths. At least 1 late effect was reported for 9% of the patients. A total of 105 SMNs were confirmed: 44 in the cHL group and 61 in the DLBCL group. According to a multivariate analysis, older age, male sex, a Karnofsky score < 90, total body irradiation (TBI) exposure, and a higher number of lines of chemotherapy before auto-HCT were risk factors for overall mortality in cHL. Risk factors in DLBCL were older age and TBI exposure. A subanalysis of 798 adolescent and young adult patients mirrored the outcomes of the overall study population. CONCLUSIONS: Despite generally favorable outcomes, 2-year survivors of auto-HCT for cHL or DLBCL have an excess late-mortality risk in comparison with the general population and experience an assortment of late complications. Cancer 2018;124:816-25. © 2017 American Cancer Society.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".