Autologous Transplantation as Consolidation for Aggressive Non-Hodgkin's Lymphoma
Bibliographic record
Abstract
BACKGROUND: The efficacy of autologous stem-cell transplantation during the first remission in patients with diffuse, aggressive non-Hodgkin's lymphoma classified as high-intermediate risk or high risk on the International Prognostic Index remains controversial and is untested in the rituximab era. METHODS: We treated 397 patients who had disease with an age-adjusted classification of high risk or high-intermediate risk with five cycles of cyclophosphamide, doxorubicin, vincristine, and prednisone (CHOP) or CHOP plus rituximab. Patients with a response were randomly assigned to receive three additional cycles of induction chemotherapy (control group) or one additional cycle of induction chemotherapy followed by autologous stem-cell transplantation (transplantation group). The primary efficacy end points were 2-year progression-free survival and overall survival. RESULTS: Of 370 induction-eligible patients, 253 were randomly assigned to the transplantation group (125) or the control group (128). Forty-six patients in the transplantation group and 68 in the control group had disease progression or died, with 2-year progression-free survival rates of 69 and 55%, respectively (hazard ratio in the control group vs. the transplantation group, 1.72; 95% confidence interval [CI], 1.18 to 2.51; P=0.005). Thirty-seven patients in the transplantation group and 47 in the control group died, with 2-year overall survival rates of 74 and 71%, respectively (hazard ratio, 1.26; 95% CI, 0.82 to 1.94; P=0.30). Exploratory analyses showed a differential treatment effect according to risk level for both progression-free survival (P=0.04 for interaction) and overall survival (P=0.01 for interaction). Among high-risk patients, the 2-year overall survival rate was 82% in the transplantation group and 64% in the control group. CONCLUSIONS: Early autologous stem-cell transplantation improved progression-free survival among patients with high-intermediate-risk or high-risk disease who had a response to induction therapy. Overall survival after transplantation was not improved, probably because of the effectiveness of salvage transplantation. (Funded by the National Cancer Institute, Department of Health and Human Services, and others; SWOG-9704 ClinicalTrials.gov number, NCT00004031.).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".