Strategies to minimize wastage of expensive drugs in expensive times: Cabazitaxel, a single center experience.
Bibliographic record
Abstract
e18297 Background: Although approved for metastatic castrate-resistant prostate cancer (mCRPC), the use of cabazitaxel has been limited due to concerns over drug wastage. In Canada, Cabazitaxel costs $96.7/mg and comes prepackaged in 60 mg single-dose vials, a quantity higher than the average prescribed and provincially reimbursed dose based on body size. Despite a discount offered by Sanofi to help cover the costs of drug wastage, many centers still cannot afford to administer it. We investigated whether aggressively batching patients (pts) on the same day, and allowing for excess drug to be used for subsequent pts can result in cost-savings. Methods: Between 09/2015 and 09/2016, all mCRPC pts receiving cabazitaxel at Princess Margaret Cancer Center were only treated on Mondays. Left over drug in opened vials was used for subsequent pts treated on the same day. Pts were dosed according to clinical parameters and BSA, at a dose of 20mg/m2 every 3 weeks. We calculated the total number of vials used daily and the amount of drug (mg) wasted. We then compared costs of the new batching system to the standard practice of opening one vial per treatment session. Results: During the trial period, 28 patients, mean age 72.3 (range 45-91) received cabazitaxel as 2nd line (23 pts) or 3rd line (5pts) therapy until disease progression or toxicity. In total, they required 117 individual treatment sessions, administered on 53 treatment days. With the batching system, 91 cabazitaxel vials were used, in comparison to 117 vials required with standard practice. As a result, 26 vials were unopened, $149,760 CAD were saved, equating to a 22.2% cost reduction over a year. In terms of wastage, $121,154 CAD of Cabazitaxel was wasted compared to $251,808 CAD wasted without batching, a difference of $130,656 CAD. Conclusions: Batching of mCRPC pts receiving cabazitaxel on the same day was feasible at a single center and significantly reduced drug wastage. This approach could be applied to centers with adequate patient volumes to save costs and reduce a key barrier to the use of cabazitaxel. A similar strategy can also be applied to other drugs across the field of oncology and has similar implications for cost-savings.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".