Prevalence and Antibiotic Resistance of Clostridioides (Clostridium difficile) In Meat and Meat Products: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Meat and meat products are introduced as one of the frequent sources of Clostridioides difficile. We aimed to determine the prevalence and antibiotic resistance of C. difficile isolates in meat and meat products using a systematic review and meta-analysis. Methods: A literature search was performed in the primary international and bibliographic databases such as MEDLINE (PubMed), Cochrane Library, Embase, Scopus, and Web of Science to achieve all articles related to the prevalence and antibiotic resistance rates from 2007 to 2022. Results: The 278 retrieved articles were reduced to 54 worldwide eligible studies after screening and matching inclusion/ exclusion criteria. C. difficile was examined in different types of samples and its resistance to 10 antibiotics. The pooled prevalence of C. difficile was 3.4% in all samples. C. difficile pooled prevalence was detected in fish, poultry, and red meat groups with 6.9%, 5.2%, and 3.2%, respectively. Regarding antibiotic resistance, the highest pooled prevalence was for ciprofloxacin (86.6%), followed by clindamycin (42.6%) and erythromycin (34%). The lowest pooled prevalence was observed in metronidazole (7.6%), vancomycin (6.6%), and chloramphenicol (6%). Conclusion: Low resistance was found to commonly used drugs for C. difficile infection (CDI) treatment. Since each antibiotic can be predisposing cause for CDI development, this finding possibly will be warning from a One Health viewpoint about the misuse of antibiotics in the chain of farm to fork including agriculture, animal husbandry and the food industry and also their injudicious use in medicine.
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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.033 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".