Antimicrobial resistance in low- and middle-income countries: current status and future directions
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: Rising rates of antimicrobial resistance (AMR) globally continue to pose agrave threat to human health. Low- and middle-income countries (LMICs) are disproportionately affected, partly due to the high burden of communicable diseases. AREAS COVERED: We reviewed current trends in AMR in LMICs and examined the forces driving AMR in those regions. The state of interventions being undertaken to curb AMR across the developing world are discussed, and the impact of the current COVID-19 pandemic on those efforts is explored. EXPERT OPINION: The dynamics that drive AMR in LMICs are inseparable from the political, economic, socio-cultural, and environmental forces that shape these nations. The COVID-19 pandemic has further exacerbated underlying factors that increase AMR. Some progress is being made in implementing surveillance measures in LMICs, but implementation of concrete measures to meaningfully impact AMR rates must address the underlying structural issues that generate and promote AMR. This, in turn, will require large infrastructural investments and significant political will.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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