From zero to zero in 100 years: gonococcal antimicrobial resistance
Bibliographic record
Abstract
The threat of antimicrobial resistance (AMR) in bacteria has been escalated to a rightful seat on the global health agenda. In September 2016, for only the fourth time in United Nations (UN) history, the UN General Assembly in New York will meet to focus on a health threat – antimicrobial resistance. Other diseases afforded this level of consultation at the UN were human immunodeficiency virus (HIV), non-communicable diseases and Ebola virus. There are grim predictions for the future in terms of AMR and health security that span income settings. These predictions challenge the premise that minor bacterial infections of childhood are innocuous, and threaten to halt the medical advancements dependant on antibiotic therapy. Those with compromised immune systems, whether endogenous or induced, will be at highest risk. The development and spread of AMR has been, and will continue to be, fanned by the relentless selection pressure of exposure to antibiotics whether used appropriately, unnecessarily or suboptimally, in human health, animal health and agriculture. The distribution of antibiotic resistant bacteria is facilitated by travel and transport. Antimicrobial resistance will affect those in the community and the hospital.
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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".