US Food and Drug Administration, Centers for Disease Control and Prevention, and National Institutes of Health Co-Sponsored Public Workshop Summary—Development Considerations of Antimicrobial Drugs for the Treatment of Gonorrhea
Bibliographic record
Abstract
There is an unmet need for developing drugs for the treatment of gonorrhea due to rapidly evolving resistance of Neisseria gonorrhoeae against antimicrobial drugs used for empiric therapy, an increase in globally reported multidrug-resistant cases, and the limited available therapeutic options. Furthermore, few drugs are under development. Development of antimicrobials is hampered by challenges in clinical trial design, limitations of available diagnostics, changes in and varying standards of care, lack of robust animal models, and clinically relevant pharmacodynamic targets. On 23 April 2021, the US Food and Drug Administration, Centers for Disease Control and Prevention, and National Institute of Allergy and Infectious Diseases of the National Institutes of Health co-sponsored a workshop with stakeholders from academia, industry, and regulatory agencies to discuss the challenges and strategies, including potential collaborations and incentives, to facilitate the development of drugs for the treatment of gonorrhea. This article provides a summary of that workshop.
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 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 | 0.018 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.013 | 0.011 |
| Insufficient payload (model declined to judge) | 0.037 | 0.009 |
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".