The Cost-Effectiveness of Combination Treatment Consisting of Either Cetuximab or Panitumumab plus FOLFIRI versus Treatment with Bevacizumab plus FOLFIRI as First-Line Treatment for KRAS Wild-Type Metastatic Colorectal Cancer Patients in Ontario
Bibliographic record
Abstract
I conducted a cost-effectiveness analysis of combination cetuximbab or panitumumab plus FOLFIRI as first-line treatment for patients with metastatic colorectal cancer (MCRC) from the perspective of the Ontario healthcare payer. I developed a Markov decision analytical model to simulate the lifetime costs and benefits of each treatment option. The model was parameterized using data collected from administrative databases in the province of Ontario and from published clinical trials. In the base case scenario, treatment consisting of bevacizumab plus FOLFIRI was found to dominate other treatment options. The ICER values were found to be sensitive to the efficacy of first-line treatment, cost of bevacizumab and cetuximab, and health utility values. In conclusion bevacizumab plus FOLFIRI for first-line treatment of patients with metastatic colorectal cancer, the current standard of care in Ontario is the most cost-effective treatment option for these patients.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".