Randomized Comparison of Gemcitabine, Dexamethasone, and Cisplatin Versus Dexamethasone, Cytarabine, and Cisplatin Chemotherapy Before Autologous Stem-Cell Transplantation for Relapsed and Refractory Aggressive Lymphomas: NCIC-CTG LY.12
Bibliographic record
Abstract
PURPOSE: For patients with relapsed or refractory aggressive lymphoma, we hypothesized that gemcitabine-based therapy before autologous stem-cell transplantation (ASCT) is as effective as and less toxic than standard treatment. PATIENTS AND METHODS: We randomly assigned 619 patients with relapsed/refractory aggressive lymphoma to treatment with gemcitabine, dexamethasone, and cisplatin (GDP) or to dexamethasone, cytarabine, and cisplatin (DHAP). Patients with B-cell lymphoma also received rituximab. Responding patients proceeded to stem-cell collection and ASCT. Coprimary end points were response rate after two treatment cycles and transplantation rate. The noninferiority margin for the response rate to GDP relative to DHAP was set at 10%. Secondary end points included event-free and overall survival, treatment toxicity, and quality of life. RESULTS: For the intention-to-treat population, the response rate with GDP was 45.2%; with DHAP the response rate was 44.0% (95% CI for difference, -9.0% to 6.7%), meeting protocol-defined criteria for noninferiority of GDP (P = .005). Similar results were obtained in a per-protocol analysis. The transplantation rates were 52.1% with GDP and 49.3% with DHAP (P = .44). At a median follow-up of 53 months, no differences were detected in event-free survival (HR, 0.99; stratified log-rank P = .95) or overall survival (HR, 1.03; P = .78) between GDP and DHAP. Treatment with GDP was associated with less toxicity (P < .001) and need for hospitalization (P < .001), and preserved quality of life (P = .04). CONCLUSION: For patients with relapsed or refractory aggressive lymphoma, in comparison with DHAP, treatment with GDP is associated with a noninferior response rate, similar transplantation rate, event-free survival, and overall survival, less toxicity and hospitalization, and superior quality of life.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".