Enhancing clinical microbiology for genomic surveillance of antimicrobial resistance implementation in Africa
Bibliographic record
Abstract
Surveillance is essential in the fight against antimicrobial resistance (AMR), to monitor the extent of resistance, inform prevention, control measures, and evaluate intervention progress. Traditional surveillance methods based on phenotypic antimicrobial susceptibility data offer important but limited insights into resistance mechanisms, transmission networks, and spread patterns of resistant bacterial strains. Fortunately, genomic technologies are increasingly accessible and can overcome these limitations. Genomics has the potential to advance traditional bacteriology in routine diagnosis and surveillance, it often relies on the initial isolation of bacterial strains from clinical specimens using conventional culture methods. Culture-based phenotypic characteristics are essential for making inferences about newly recognized genomic patterns. The Africa CDC Pathogen Genomics Initiative (Africa PGI) aims to enhance disease surveillance and public health partnerships through integrated, cross-continent laboratory networks equipped with the tools, human resource capacity and data infrastructure to fully leverage critical genomic sequencing technologies. For genomic surveillance of AMR, it is essential to optimize routine clinical microbiology laboratory services that are weak in many African countries. In this review, we outline shortcomings in clinical microbiology laboratories across Africa that compromise pathogen genomic epidemiology. We emphasize the necessity of investing in bacteriology and enhancing leadership capacity to fully capitalize on the advantages offered by genomic antimicrobial resistance (AMR) surveillance.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".