National Trends In Spending On And Use Of Oral Oncologics, First Quarter 2006 Through Third Quarter 2011
Bibliographic record
Abstract
Oral prescription drugs are an increasingly important treatment option for cancer. Yet contemporaneous US trends in spending on anticancer drugs known as oral oncologics have not been described. Using nationally representative data, we describe trends in national spending on and use of forty-seven oral oncologics between the first quarter of 2006 and the third quarter of 2011. Average quarterly national spending on oral oncologics increased 37 percent, from $940.3 million to $1.4 billion in 2012 dollars, a significant change. Average quarterly use of oral oncologics in the same time period measured in extended units increased at a significant pace but more slowly than spending (10 percent). Within this broader trend, differences in spending among categories of oral oncologics were observed. High levels of and increases in both spending and use were concentrated among new brand-name and patent-protected oral oncologics, including second-generation tyrosine kinase inhibitors used to treat chronic myelogenous leukemia. Decreased spending but increased use was observed among oral oncologics that lost patent protection during the study period and were available in generic form, including hormonal therapies used to treat breast and prostate cancers. Spending on new and patent-protected oral oncologics and associated price increases are significant drivers of increased spending.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".