Minimizing drug wastage (DW) and cost of cabazitaxel used to treat metastatic castrate-resistant prostate cancer (mCRPC).
Bibliographic record
Abstract
6550 Background: Cabazitaxel is indicated for mCRPC, but is associated with substantial DW and financial strain on hospital budgets. It is only available in single-dose 60mg vials and has short reconstituted drug stability of < 24 hours. We aimed to determine feasibility and cost savings of an aggressive batching strategy to facilitate vial sharing of Cabazitaxel. Methods: Our mitigation strategy was to administer Cabazitaxel 20mg/m 2 q3-weekly (without prophylactic G-CSF) on a single weekday whenever possible. Drug was prepared after patient (pt) arrival. Remaining amount from each vial was saved for subsequent pts on the same day. Amount administered, discarded and number of (#) vials used were obtained from pharmacy records. We estimated drug cost without batching by assigning 1 vial/treatment, and drug cost with batching from the actual # vials used. Cost of DW was determined from the amount discarded. All cost calculations were based on market price ($96.7CAD/mg) accounting for Sanofi’s discount incentive (5 vials for the price of 4), allowing a real-world cost assessment. Results: Between 09/2015 and 09/2018, 74 pts received 404 Cabazitaxel treatments on 164 days using 319 vials. Multiple pts were batched on 68% treatment days. Every 3 pts batched saved 1 vial. Average dose/treatment was 37mg (20-45mg). Among 10 treatment cancellations, prepared drug was administered for subsequent pts in 9 cases. Drug and DW costs over the 3-year period with and without batching are shown in Table. Conclusions: Batching ≥3 pts on a single weekday was feasible and significantly lowered drug cost of Cabazitaxel by reducing wastage. This strategy could help mitigate costs associated with wastage for other oncology drugs. [Table: see text]
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".