Industry incentives and antibiotic resistance: an introduction to the antibiotic susceptibility bonus
Bibliographic record
Abstract
The scarcity of novel antibiotic compounds in a time of increasing resistance rates has begun to ring alarm bells at the highest echelons of government. Large new financial incentives to accelerate antibiotic research and development, such as market entry rewards (MERs), are being considered. However, there is little focus on how to sustain the efficacy of new, promising antibiotics reaching the market. Currently, inappropriate use of antibiotics is commonplace, which has accelerated resistance development. In an attempt to halt this trend, antibiotic stewardship policies are being implemented in many resource-rich settings. Unfortunately, this has not yet had an impact on the amount of antibiotics being prescribed globally. One important hurdle is misalignment of incentives. While governments and health services are incentivized to promote prudent use of this common good, pharmaceutical companies are incentivized to increase volume of sales to maximize profits. This problem must be addressed or else the major efforts going into developing new antibiotics will be in vain. In this paper we outline an approach to realign the incentives of pharmaceutical companies with wider antibiotic conservation efforts by making a staged bonus a component of an MER for antibiotic developers when resistance to their drug remains low over time. This bonus could address the lack of stewardship focus in any innovation-geared incentive.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".