Gemcitabine/Dexamethasone/Cisplatin vs Cytarabine/Dexamethasone/Cisplatin for Relapsed or Refractory Aggressive-Histology Lymphoma: Cost-Utility Analysis of NCIC CTG LY.12
Bibliographic record
Abstract
BACKGROUND: The NCIC CTG LY.12 study showed that gemcitabine, dexamethasone, and cisplatin (GDP) were noninferior to dexamethasone, cytarabine, and cisplatin (DHAP) in patients with relapsed or refractory aggressive histology lymphoma prior to autologous stem cell transplantation. We conducted an economic evaluation from the perspective of the Canadian public healthcare system based on trial data. METHODS: The primary outcome was an incremental cost utility analysis comparing costs and benefits associated with GDP vs DHAP. Resource utilization data were collected from 519 Canadian patients in the trial. Costs were presented in 2012 Canadian dollars and disaggregated to highlight the major cost drivers of care. Benefit was measured as quality-adjusted life-years (QALYs) based on utilities translated from prospectively collected quality-of-life data. All statistical tests were two-sided. RESULTS: The mean overall costs of treatment per patient in the GDP and DHAP arms were $19 961 (95% confidence interval (CI) = $17 286 to $24 565) and $34 425 (95% CI = $31 901 to $39 520), respectively, with an incremental difference in direct medical costs of $14 464 per patient in favor of GDP (P < .001). The predominant cost driver for both treatment arms was related to hospitalizations. The mean discounted quality-adjusted overall survival with GDP was 0.161 QALYs and 0.152 QALYs for DHAP (difference = 0.01 QALYs, P = .146). In probabilistic sensitivity analysis, GDP was associated with both cost savings and improved quality-adjusted outcomes compared with DHAP in 92.6% of cost-pair simulations. CONCLUSIONS: GDP was associated with both lower costs and similar quality-adjusted outcomes compared with DHAP in patients with relapsed or refractory lymphoma. Considering both costs and outcomes, GDP was the dominant therapy.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".