Funding Antibiotic Innovation with Vouchers: Recommendations on How to Strengthen a Flawed Incentive Policy
Bibliographic record
Abstract
A serious need to spur antibiotic innovation has arisen because of the lack of antibiotics to combat certain conditions and the overuse of other antibiotics leading to greater antibiotic resistance. In response to this need, proposals have been made to Congress to fund antibiotic research through a voucher program for new antibiotics, which would delay generic entry for any drug, even potential blockbuster lifesaving generics. We find this proposal to be inefficient, in part because of the mismatch between the private value of the voucher and the public value of the antibiotic innovation. However, vouchers have the political advantage in the United States of being able to raise sufficient amounts of money without annual appropriations from Congress. We propose that if antibiotic vouchers are to be considered, the design should include dollar and time caps to limit their volatility, sufficient advance notice to protect generic manufacturers, and market-based linkages between the value of the voucher and the value of the antibiotic innovation. We also explore a second option: The federal government could auction vouchers to the highest bidders and use the money to create an antibiotics innovation fund.
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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 | 0.089 | 0.256 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.020 | 0.040 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.066 | 0.027 |
| Insufficient payload (model declined to judge) | 0.023 | 0.005 |
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".