Antimicrobial Resistance in Neisseria gonorrhoeae: Proceedings of the STAR Sexually Transmitted Infection—Clinical Trial Group Programmatic Meeting
Bibliographic record
Abstract
The goal of the Sexually Transmitted Infection Clinical Trial Group's Antimicrobial Resistance (AMR) in Neisseria gonorrhoeae (NG) meeting was to assemble experts from academia, government, nonprofit and industry to discuss the current state of research, gaps and challenges in research and technology and priorities and new directions to address the continued emergence of multidrug-resistant NG infections. Topics discussed at the meeting, which will be the focus of this article, include AMR NG global surveillance initiatives, the use of whole genome sequencing and bioinformatics to understand mutations associated with AMR, mechanisms of AMR, and novel antibiotics, vaccines and other methods to treat AMR NG. Key points highlighted during the meeting include: (i) US and International surveillance programs to understand AMR in NG; (ii) the US National Strategy for combating antimicrobial-resistant bacteria; (iii) surveillance needs, challenges, and novel technologies; (iv) plasmid-mediated and chromosomally mediated mechanisms of AMR in NG; (v) novel therapeutic (eg, sialic acid analogs, factor H [FH]/Fc fusion molecule, monoclonal antibodies, topoisomerase inhibitors, fluoroketolides, LpxC inhibitors) and preventative (eg, peptide mimic) strategies to combat infection. The way forward will require renewed political will, new funding initiatives, and collaborations across academic and commercial research and public health programs.
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 | 0.030 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".