Resistance among Gram-negative ESKAPE pathogens isolated from hospitalized patients with intra-abdominal and urinary tract infections in Latin American countries: SMART 2013–2015
Bibliographic record
Abstract
Gram-negative ESKAPE pathogens (Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter species) are important etiologic agents of nosocomial infection that are frequently resistant to broad-spectrum antimicrobial agents. Gram-negative ESKAPE pathogens were collected from hospitalized patients in 11 Latin American countries from 2013 to 2015 as part of the Study for Monitoring Antimicrobial Resistance Trends (SMART) global surveillance program. In total, 2113 isolates from intra-abdominal infections (IAI) and 970 isolates from urinary tract infections (UTI) were tested against antimicrobial agents using standardized CLSI broth microdilution methodology. Of the agents tested, amikacin demonstrated the highest rates of susceptibility (%) for K. pneumoniae (92.2, 92.3), Enterobacter spp. (97.5, 92.1), and P. aeruginosa (85.3, 75.2) isolates from both IAI and UTI, respectively. Ertapenem (68.5, 62.6) and imipenem (79.2, 75.9) showed substantially higher rates of susceptibility (%) than other β-lactams, including piperacillin-tazobactam (35.9, 37.4) against ESBL-positive isolates of K. pneumoniae from IAI and UTI, respectively. Rates of susceptibility to all agents tested against A. baumannii were ≤30.9%. Gram-negative ESKAPE pathogens isolated from Latin America demonstrated compromised in vitro susceptibility to commonly prescribed broad-spectrum, parenteral antimicrobial agents. Continued surveillance is warranted. New antimicrobial agents with potent activity against Gram-negative ESKAPE pathogens are urgently needed.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".