The potential drug cost impact of nivolumab (N) in patients with advanced/metastatic gastric cancer (GC) or gastroesophageal junction cancer (GEJC) in Canada.
Bibliographic record
Abstract
101 Background: Advanced/metastatic GC and GEJC is associated with poor survival outcomes. Systemic treatment options are limited in patients having had at least two lines of chemotherapy. Immune checkpoint inhibitors (ICIs) appear to be a promising therapeutic option in these patients. The role of predictive biomarkers to response to ICIs remains to be fully elucidated. This may ultimately inform the utility and thus potentially the drug cost incurred by ICIs. The ATTRACTION-2 phase III study using N in heavily pretreated advanced GC and GEJC patients improved outcomes. The use of ICIs 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 advanced de novo and relapsed cases diagnosed in 2017 and subsequently treated in the third line in Canada was undertaken. A cost estimate for N treatment in earlier lines was also evaluated. N cost per patient was calculated based on treatment indication, duration of treatment, 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 $15,770. The N drug cost in the third line setting is estimated at $5.9 million (M) for GC and $2.4M for GEJC, total $8.4M (IQR $3.9M-$18.7M). For first line and second line N in eligible pts respectively: potential drug cost is $23.7M, $11.8M for GC and $9.7M, $4.9M for GEJC. A sensitivity analysis was performed. Conclusions: ICIs potentially add a drug cost burden to the publically funded Canadian healthcare system. As biomarkers predictive of response evolve and patients are treated accordingly, the drug cost burden may lessen. Potential earlier line use and a longer duration of therapy will add to the estimated budgetary impact.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".