A real-world data approach to determine the optimal dosing strategy for pembrolizumab
Bibliographic record
Abstract
INTRODUCTION: Cancer drug therapy costs continue to rise and threaten the sustainability of Canada's public healthcare system. Previous studies have calculated potential savings utilizing different dosing regimens of cancer treatments. Our objectives were to determine the financial impact of drug wastage and to explore cost-effective dosing regimens for pembrolizumab. METHODS: This was a retrospective study reviewing data for non-small cell lung cancer and melanoma patients at all six BC Cancer Regional Centres during fiscal years 2017 and 2018. Pembrolizumab waste amounts recorded in pharmacy wastage logs were totalled. Estimates of the number of vials used were compared between vial sharing and non-vial sharing practices to determine the cost differences. Costs for dosing regimens used during fiscal years 2017 and 2018 were compared to 2 mg/kg weight-based dosing (to a maximum of 200 mg), 2 mg/kg dosing rounding down within 5% and 10%, and flat dosing of 200 mg. RESULTS: There were a total of 202 non-small cell lung cancer and 182 melanoma patients with 2948 doses dispensed. Documented wastage was valued at $1,829,047.44 (8.65%) and across all six centres, vial sharing could reduce costs by $3,207,600.00 using the 100 mg vials. Compared to fiscal years 2017 and 2018, 2 mg/kg dosing (to a maximum of 200 mg) was the most cost-effective, decreasing costs by $222,719.20; flat dosing of 200 mg was the most expensive, increasing costs by $6,625,260.40. CONCLUSIONS: Having smaller vial sizes, practicing vial sharing, and using weight-based dosing all improve cost savings. Further investigations on the allocation of resources to optimize drug use and minimize wastage are needed.
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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 | 0.012 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".