Peruvian scientific production on antimicrobial-resistant bacteria prioritized by WHO
Bibliographic record
Abstract
Introduction: Antimicrobial resistance (AMR) is a worldwide public health crisis. The World Health Organization (WHO) established a priority list of resistant bacteria to guide research and alternatives for improvement. Objective: To describe the scientific production of Peru on AMR of bacteria prioritized by the World Health Organization, between 2012 and 2021. Methods: Observational descriptive study of bibliometric type in journals indexed in Scopus during the period 2012-2021. The selection of studies and data extraction were performed manually in duplicate. Resistant bacteria studied were classified based on priority (critical, high, and medium). Results: A total of 118 articles were included. During the period 2014-2021, the number of publications increased. The articles published in English accounted for 61.9%, 98.3% had their affiliation in Peru, and 77.1% were conducted in Lima. Most publications focused on bacteria of critical priority than high and medium priority. A total of 79.7% sought to determine prevalence or characterize and 26.1% referred to funding from Peruvian institutions. Conclusions: Peruvian scientific production on AMR has increased in recent years and there are more publications on critical priority bacteria. However, these studies are centered in Lima and only a quarter of them have been financed by a Peruvian entity.
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 | 0.025 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.068 | 0.084 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".