A One Health genomic approach to antimicrobial resistance is essential for generating relevant data for a holistic assessment of the biggest threat to public health
Bibliographic record
Abstract
© 2019 Microbiology Australia. All rights reserved. Antimicrobial resistance (AMR) threatensmodernmedicine asweknow it.AMR infectionsmay ultimately beuntreatable and routine surgeries will become inherently risky1. By 2050 more people may die of drug-resistant infections (DRIs) every year than of cancer, which equates to more than 10 million annual deaths globally2 and the World Bank has estimated that AMR could cost the global economy 1 trillion every year after 2030.DRIs alsolead to an increase in the length of hospital stays, the use of more toxic or costly antibiotics and an increased likelihood of death3. BRIC nations (Brazil, Russia, India, China) and socio-economically challenged countries and people who already have higher rates of infectious diseases will feel the greatest impact2. Indeed, AMR has been likened to the 2008 global financial crisis on an annual repeat cycle.Thatis because the effects of AMR are not just confined to the human medical sector. The veterinary sector is also reliant on the availability of antimicrobials to treat infectious diseases in companion and food-producing animals.
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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 | 0.014 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.018 | 0.008 |
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".