Contribution of Food to the Human Health Burden of Antimicrobial Resistance
Bibliographic record
Abstract
The impact of foodborne antimicrobial resistance (AMR) on the human health burden of AMR infections is unknown. The aim of this review was to evaluate and summarize the scientific literature investigating all potential sources of human AMR infections related to food. A literature search was conducted in Embase (Ovid) and MEDLINE (Ovid) databases to identify appropriate studies published between 2010 and 2023. The results of the search were reviewed and categorized based on the primary subject matter. Key concepts from each category are described from the perspective of food safety as a public health objective. The search yielded 3457 references, 1921 remained after removal of duplicates, abstracts, editorials, comments, notes, retractions, and errata. No properly designed source attribution studies were identified, but 383 journal articles were considered relevant and were classified into eight subcategories and discussed in the context of four streams of evidence: prevalence data, epidemiological studies, outbreak investigations and human health impact estimates. There was sufficient evidence to conclude that AMR genes, whether present in pathogenic or nonpathogenic bacteria, constitute a foodborne hazard. The level of consumer risk owing to this hazard cannot be accurately estimated based on the data summarized here. Key gaps in the literature are noted.
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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".