Antimicrobial resistance in low- and middle-income countries: current status and future directions
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.
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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".