Impact of novel chronic lymphocytic leukemia drugs on public spending.
Bibliographic record
Abstract
103 Title: Impact Of Novel Chronic Lymphocytic Leukemia Drugs On Public Spending Background: Chronic lymphocytic leukemia (CLL) is a common hematologic malignancy that mainly affects the elderly. Over the past five years, the treatment pathway for CLL has dramatically changed with the emergence of multiple novel agents. In Ontario, Canada, the Ontario Drug Benefit (ODB) program and the New Drug Funding Program (NDFP) primarily provide public coverage for CLL drugs. Given advances in treatment, we evaluated utilization trends for publicly-funded CLL drugs over a five year period (fiscal years 2012/13 to 2016/17). Methods: Drugs with primary CLL indications funded under the two public funding programs (i.e., ODB and NDFP) by 16/17 were included (n = 6). Claims and costs data were obtained from the Institute for Clinical Evaluative Sciences and Cancer Care Ontario‘s datasets. Results: Over five years, expenditures on CLL drugs have increased approximately ten-fold (1000 percent), reaching nearly CAD 43 million (i.e., USD 32.8 million) in 16/17. In the front-line setting, spending on rituximab remained consistent over the five years. Spending on single agent bendamustine decreased with the introduction of obinutuzumab which became the main cost driver by 16/17. In the previously-treated CLL population, ibrutinib has dominated expenditures since it was funded in July 2015. By 16/17, ibrutinib accounted for approximately ninety eight percent of spending in the previously-treated population. Conclusions: In the past five years, public spending on CLL drugs increased rapidly and substantially with the introduction of four novel agents, and may continue to evolve as Canadian provinces consider funding additional indications or newer agents. These findings warrant exploring whether the incremental spending is providing survival benefit or improvement in patients’ quality of life in a real-world setting. Declaration of funding: None
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".