Bibliographic record
Abstract
The magnitude of the increasing problem of resistance really takes all its meaning when appraised side-by-side with the paucity of new antimicrobials reaching the market (1). Several factors have contributed to making antimicrobial discovery less fashionable nowadays. The gigantic costs of bringing a new compound to market, from the identification of a promising target at the preclinical stages, to the final clinical trials and approval, are clearly a strong deterrent. This emphasizes the difficulty in realizing an interesting financial return, given that antimicrobials are used for diseases occurring on a very short timespan (compared with the treatment of chronic conditions) and that regulatory requirements are strict (2). In the United States, in an attempt to stimulate the discovery of new antimicrobials, the Generating Antibiotic Incentives Now (GAIN) Act has been passed by the Obama administration. Among the provisions of the Act, sponsors developing new antibiotics may benefit from the following incentives: five additional years of market exclusivity, priority review, fast-track approval and updated guidance (3). The impact of the GAIN Act is difficult to evaluate such a short time after its implementation, but considering the high costs of development and evaluation, five additional years of market exclusivity appears to be a small upgrade to really provide incentive to pharmaceutical companies to invest in this field.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".