The Super-Wicked Problem of Antimicrobial Resistance
Bibliographic record
Abstract
Abstract Antimicrobial resistance (AMR) – the progressive process by which microbes, such as bacteria, through evolutionary, environmental and social factors develop the ability to become resistant to drugs that were once effective at treating them – is a threat from which no one can escape. It is one of the largest threats to clinical and global health in the twenty-first century – inflicting monumental health, economic and social consequences. All persons locally and globally, and even all future persons yet to come into existence, all suffer the shared, interdependent vulnerability to this threat that will have a substantial impact on all aspects of our lives. For example, while reliable data are hard to find, the European Centre for Disease Prevention and Control (ECDC) has conservatively estimated that, in Europe alone, AMR causes additional annual cost to health care systems of at least €1.5 billion, and is responsible for around 25,000 deaths per year. Furthermore, AMR significantly increases the cost of treating bacterial infections with an increase in length of hospital stays and average number of re-consultations, as well as the resultant lost productivity from increased morbidity. With a combined cost of up to $100 trillion to the global economy – pushing a further 28 million people into extreme poverty – this is one of the most pressing challenges facing the world. Most troublingly, if we do not succeed in diminishing the progression of AMR, there is the very real potential for it to threaten common procedures and treatments of modern medicine, including the safety and efficacy of surgical procedures and immunosuppressing chemotherapy. Some experts are warning that we may soon be ushering in a post-antibiotic area.
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 | 0.024 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.027 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.012 | 0.020 |
| Insufficient payload (model declined to judge) | 0.014 | 0.006 |
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".