Lessons learned from COVID-19 for the post-antibiotic future
Bibliographic record
Abstract
INTRODUCTION: COVID-19 has rapidly and radically changed the face of human health and social interaction. As was the case with COVID-19, the world is similarly unprepared to respond to antimicrobial resistance (AMR) and the challenges it will produce. COVID-19 presents an opportunity to examine how the international community might better respond to the growing AMR threat. MAIN BODY: The impacts of COVID-19 have manifested in health system, economic, social, and global political implications. Increasing AMR will also present challenges in these domains. As seen with COVID-19, increasing healthcare usage and resource scarcity may lead to ethical dilemmas about prioritization of care; unemployment and economic downturn may disproportionately impact people in industries reliant on human interaction (especially women); and international cooperation may be compromised as nations strive to minimize outbreaks within their own borders. CONCLUSION: AMR represents a slow-moving disaster that offers a unique opportunity to proactively develop interventions to mitigate its impact. The world's attention is currently rightfully focused on responding to COVID-19, but there is a moral imperative to take stock of lessons learned and opportunities to prepare for the next global health emergency.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.012 | 0.017 |
| Insufficient payload (model declined to judge) | 0.024 | 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".