Potential drug cost impact of nivolumab (N) in Canada in patients with DNA mismatch repair deficient (dMMR) metastatic colorectal cancer (mCRC).
Bibliographic record
Abstract
797 Background: The overall 5 year survival for mCRC remains poor (14%) despite the sequential use of chemotherapy and biologic agents. Immunotherapy is a promising treatment option in tumors with a high mutational burden. This includes mCRC DNA mismatch repair deficient tumors (dMMR). These have upregulation of immune checkpoints and a poorer prognosis. The CheckMate 142 phase II trial in pretreated dMMR mCRC patients showed durable responses and disease control across a number of subgroups including BRAF mutant tumors. The use of immunotherapy has an anticipated budgetary impact on health care systems within the context of this potentially funded utilization of N. Methods: An estimation of the N drug cost alone for new cases diagnosed in 2017 and treated upon relapse in Canada was undertaken. A cost estimate for N treatment in the first line of dMMR mCRC in relapses and de novo was undertaken should this be a future option. N cost per patient was calculated based on treatment indication, median number of cycles, standard dose/schedule. The analysis was performed in Canadian dollars ($) and assumed complete drug delivery and uncomplicated cycles. The cost of N was obtained from the pan Canadian Oncology Drug Review (pCODR) cost for N in lung cancer. The number of target patients and N utilization was derived from constructed schema to give a budget impact estimate. Results: Estimated N cost per treated patient is $91,667. The N drug cost for prior second line and third line relapsed chemotherapy treated patients respectively ranges from $23.6 Million (M) – $36.4M and $12.7M – $17.7M. For 1st line N: the cost of treating early stage dMMR CRC which subsequently recurs would be $49.1M and the cost for treatment of dMMR de novo mCRC would be $24.6M. A sensitivity analysis was performed. Conclusions: Immunotherapy drug costs in dMMR mCRC potentially add a substantial cost burden to the publicly funded Canadian healthcare system. As data evolves, longer duration of therapy and potential first line use will add further to the estimated budgetary impact.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".