Lessons learned from COVID-19 for the post-antibiotic future
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it