Vaccination is an integral strategy to combat antimicrobial resistance
Bibliographic record
Abstract
Pleaseconfirmthatallheadinglevelsarerepresentedcorrectly:Antimicrobial-resistant bacterial infections pose a significant challenge to health worldwide.In 2019, there were an estimated 1.95 million deaths and 47.9 million lost disability-adjusted life-years attributable to antimicrobial resistance (AMR) [1].Over the last 30 years, there has been a stall in the development of new antibiotics while incidence rates of AMR climb [2].The global AMR crisis is on-track to cause approximately 10 million deaths annually by 2050 [1].Several pathogens contribute to this, including Escherichia coli, Klebsiella pneumoniae, Streptococcus pneumoniae, Haemophilus influenzae, and Mycobacterium tuberculosis [1,3].Targeted interventions to combat AMR, such as vaccines, are essential in conjunction with the continued pursuit of antibiotic discovery and engagement with equitable antibiotic stewardship policies.Many bacterial vaccines are already included in publicly funded vaccination programs and developing technologies in vaccine platforms has the potential to address AMR equitably and effectively [4].Vaccines have the potential to reduce antibiotic usage at the population level, reduce the spread of bacterial resistance determinants, and decrease transmission of resistant bacteria (Fig 1).
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".