Estimating The Cost Of Delayed Generic Drug Entry To Medicaid
Bibliographic record
Abstract
Delays in market entry of generic drugs are common. This study sought to identify the prevalence of delayed entry, the reasons for the delays, and the delays' effects on Medicaid spending in a recent cohort of brand-name medications. We estimated excess Medicaid spending in 2010-16 in the delayed quarter-years after accounting for market average predictions of brand-name market share, ratios of generic to brand-name prices, and Medicaid rebates (60 percent for brand-name and 15 percent for generic drugs). Among sixty-nine brand-name drugs that were predicted to lose market exclusivity, generic entry occurred either before or within a quarter-year of the expected date for thirty-eight products (55 percent), was delayed by more than one quarter for twenty products (29 percent), and did not occur for eleven products (16 percent). For the thirty-one products (45 percent) for which generic entry was delayed by more than one quarter or did not occur, Medicaid spent an estimated excess of $761 million over seven years ($109 million annually). Patent litigation was the most common cause of generic entry delays. Policies that expedite the resolution of patent challenges are needed to ensure the timely entry of generic drugs.
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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 | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".