The impact of cancer drug wastage on economic evaluations
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: The objective of this study was to determine the impact of modeling cancer drug wastage in economic evaluations because wastage can result from single-dose vials on account of body surface area- or weight-based dosing. METHODS: Intravenous chemotherapy drugs were identified from the pan-Canadian Oncology Drug Review (pCODR) program as of January 2015. Economic evaluations performed by drug manufacturers and pCODR were reviewed. Cost-effectiveness analyses and budget impact analyses were conducted for no-wastage and maximum-wastage scenarios (ie, the entire unused portion of the vial was discarded at each infusion). Sensitivity analyses were performed for a range of body surface areas and weights. RESULTS: Twelve drugs used for 17 indications were analyzed. Wastage was reported (ie, assumptions were explicit) in 71% of the models and was incorporated into 53% by manufacturers; this resulted in a mean incremental cost-effectiveness ratio increase of 6.1% (range, 1.3%-14.6%). pCODR reported and incorporated wastage for 59% of the models, and this resulted in a mean incremental cost-effectiveness ratio increase of 15.0% (range, 2.6%-48.2%). In the maximum-wastage scenario, there was a mean increase in the incremental cost-effectiveness ratio of 24.0% (range, 0.0%-97.2%), a mean increase in the 3-year total incremental budget costs of 26.0% (range, 0.0%-83.1%), and an increase in the 3-year total incremental drug budget cost of approximately CaD $102 million nationally. Changing the mean body surface area or body weight caused 45% of the drugs to have a change in the vial size and/or quantity, and this resulted in increased drug costs. CONCLUSIONS: Cancer drug wastage can increase drug costs but is not uniformly modeled in economic evaluations. Cancer 2017;123:3583-90. © 2017 American Cancer Society.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it