Economic evaluation of arsenic trioxide compared to all‐trans retinoic acid + conventional chemotherapy for treatment of relapsed acute promyelocytic leukemia in Canada
Bibliographic record
Abstract
OBJECTIVES: Acute promyelocytic leukemia (APL) is an uncommon type of acute leukemia characterized by high early mortality. Current first-line treatments include all-trans retinoic acid (ATRA), anthracyclines, and other conventional chemotherapies (CTs). Although APL is generally associated with a good prognosis, about 20% of patients who achieve remission subsequently relapse and are resistant to the previously administrated treatment. The objective of this study was to assess, from a Canadian perspective, the economic impact of arsenic trioxide (ATO) compared to ATRA+CT for treatment of patients with relapsed/refractory APL. METHODS: The cost-effectiveness of ATO compared to ATRA+CT for treating patients with relapsed/refractory APL was assessed over a lifetime horizon using a Markov model. The model considers five health states: induction, second remission, treatment failure or relapse, postfailure, and death. Markov cycle length was 1 month for the first 24 months and 1 yr thereafter. The model also takes into account the incidence of grade 3-4 adverse events reported in clinical trials. Analyses were conducted from a Canadian Ministry of Health (MoH) and a societal perspective. RESULTS: Compared to ATRA+CT, ATO was associated with incremental cost-effectiveness ratios of $ 20,551/quality-adjusted life year (QALY) from a MoH perspective and $ 22,219/QALY from a societal perspective. Results of the probabilistic sensitivity analysis indicated that ATO is a cost-effective strategy in 99.27% and 98.98% of the simulations from a MoH and a societal perspective, respectively. CONCLUSIONS: This economic evaluation demonstrates that ATO is a cost-effective strategy compared to ATRA+CT for treatment of patients with relapsed/refractory APL in Canada.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".