Cost-Effectiveness of Systemic Therapies for Metastatic Pancreatic Cancer
Bibliographic record
Abstract
PURPOSE: Gemcitabine and capecitabine (gem-cap), gemcitabine and erlotinib (gem-e), and folfirinox (5-fluorouracil-leucovorin-irinotecan-oxaliplatin) are new treatment options for metastatic pancreatic cancer, but they are also more expensive and potentially more toxic than gemcitabine alone (gem). We conducted a cost-effectiveness analysis of these treatment options compared with gem. METHODS: A Markov model was constructed to examine costs and outcomes of gem-cap, gem-e, folfirinox, and gem in patients with metastatic pancreatic cancer from the perspective of a government health care plan. Ontario health economic and costing data (2010 Canadian dollars) were used. Efficacy data for the treatments were obtained from the published literature. Resource utilization data were derived from a chart review of consecutive metastatic patients treated for pancreatic cancer at Princess Margaret Hospital, Toronto, Ontario, 2008-2009, and supplemented with data from the literature. Utilities were obtained by surveying medical oncologists across Canada using the EQ-5D. Incremental cost-effectiveness ratios (icers) were calculated. RESULTS: The icers for gem-cap, gem-e, and folfirinox compared with gem were, respectively, CA$84,299, CA$153,631, and CA$133,184 per quality-adjusted life year (qaly). The model was driven mostly by drug acquisition costs. Given a willingness-to-pay (wtp) threshold greater than CA$130,000/qaly, folfirinox was most cost-effective treatment. When the wtp threshold was less than CA$80,000/qaly, gem alone was most cost-effective. The gem-e option was dominated by the other treatments. CONCLUSIONS: The most cost-effective treatment for metastatic pancreatic cancer depends on the societal wtp threshold. If the societal wtp threshold were to be relatively high or if drug costs were to be substantially reduced, folfirinox might be cost-effective.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".