Olaparib versus Placebo in Maintenance Treatment of Germline BRCA-Mutated Metastatic Pancreatic Cancer: A Cost–Utility Analysis from the Canadian Public Payer’s Perspective
Bibliographic record
Abstract
Pancreatic cancer has an annual incidence of 2/10,000 in Canada, with a one-year mortality rate greater than 80%. In the absence of a cost-effectiveness analysis in Canada, this study's objective was to assess the cost-effectiveness of olaparib versus a placebo in adult patients with deleterious or suspected deleterious BRCA metastatic pancreatic adenocarcinoma, who did not show any progression for at least 16 weeks with first-line platinum-based chemotherapy. A partitioned survival model with a 5-year time horizon was adopted to estimate the costs and effectiveness. All of the costs were extracted from the public payer's available resources, effectiveness data were obtained from the POLO trial, and Canadian studies were used for utility inputs. Probabilistic sensitivity analyses and scenario analyses were performed. The total costs of olaparib and the placebo over five years were CAD 179,477 and CAD 68,569, with overall quality-adjusted life-years (QALYs) of 1.70 and 1.36, respectively. The incremental cost-effectiveness ratio (ICER) of the olaparib group compared with the placebo was CAD 329,517 per QALY. With a commonly cited willingness to pay (WTP) threshold of CAD 50,000 per QALY, the drug does not achieve acceptable cost-effectiveness mainly due to the high price of the medication and insufficient impact on the overall survival of patients with metastatic pancreatic cancer.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".