Antimicrobial Resistance of Listeria monocytogenes from Animal Foods to First- and Second-Line Drugs in the Treatment of Listeriosis from 2008 to 2021: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
First-line drugs for the treatment of listeriosis are the same around the world, but particular conditions might reduce their efficacy, including antimicrobial resistance. Therefore, this study aimed to verify, based on a systematic review and meta-analysis, whether the prevalence of antimicrobial resistance in Listeria monocytogenes from animal foods is higher for first- or second-line antimicrobials. From the total of 302 identified studies, 16 met all the eligibility criteria from 2008 to 2021 and were included in this meta-analysis. They comprised a dataset of 1152 L. monocytogenes isolates, obtained from different animal food products, food processing environment, and live animals. The included studies were developed in South America (n = 5), Europe (n = 4), Asia (n = 3), Africa (n = 2), and North America (n = 2), testing a total of 35 different antimicrobials, 11 of them classified as first-line drugs. Complete lack of antimicrobial resistance across the studies (all L. monocytogenes isolates tested as susceptible) was only observed for linezolid, while widespread antimicrobial resistance (all L. monocytogenes isolates tested resistant) was described for amoxicillin, benzylpenicillin, cefoxitin, fusidic acid, imipenem, sulfamethoxazole, and vancomycin. Overall, the meta-analysis results indicated no evidence that antimicrobial resistance in L. monocytogenes isolated from animal-based food is higher for first-line antimicrobials compared to second-line compounds ( p = 0.37 ). A greater volume of publication, together with better characterization of the isolates, is still needed for a more precise estimate of the real prevalence of antimicrobial resistance in L. monocytogenes.
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 | 0.013 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.048 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".