Influence of Age and Histology on Outcome in Adult Non-Hodgkin Lymphoma Patients Undergoing Autologous Hematopoietic Cell Transplantation (HCT): A Report from The Center For International Blood & Marrow Transplant Research (CIBMTR)
Bibliographic record
Abstract
To compare the clinical outcomes of older (age > or =55 years) non-Hodgkin lymphoma (NHL) patients with younger NHL patients (<55 years) receiving autologous hematopoietic cell transplantation (HCT) while adjusting for patient-, disease-, and treatment-related variables, we compared autologous HCT outcomes in 805 NHL patients aged > or =55 years to 1949 NHL patients <55 years during the years 1990-2000 using data reported to the Center for International Blood and Marrow Transplant Research (CIBMTR). In multivariate analysis, older patients with aggressive histologies were 1.86 times (95% confidence interval [CI] 1.43-2.43, P < .001) more likely than younger patients to experience treatment-related mortality (TRM). Relative death risks were 1.33 times (CI 1.04-1.71, P = .024) and 1.50 times (CI 1.33-16.9, P < .001) higher in older compared to younger patients with follicular grade I/II and aggressive histologies, respectively. Autologous HCT in older NHL patients is feasible, but most disease-related outcomes are statistically inferior to younger patients. Studies addressing supportive care particular to older patients, who are most likely to benefit from this approach, are recommended.
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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 | 0.001 |
| 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 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".