Impact of point sources on antibiotic resistance genes in the natural environment: a systematic review of the evidence
Bibliographic record
Abstract
There is a growing concern about the role of the environment in the dissemination of antibiotic resistant bacteria (ARB) and antibiotic resistance genes (ARG). In this systematic review, we summarize evidence for increases of ARG in the natural environment associated with potential sources of ARB and ARG such as agricultural facilities and wastewater treatment plants. A total of 5247 citations were identified, including studies that ascertained both ARG and ARB outcomes. All studies were screened for relevance to the question and methodology. This paper summarizes the evidence only for those studies with ARG outcomes (n = 24). Sixteen studies were at high (n = 3) or at unclear (n = 13) risk of bias in the estimation of source effects due to lack of information or failure to control for confounders. Statistical methods were used in nine studies; three studies assessed the effect of multiple sources using modeling approaches, and none reported effect measures. Most studies reported higher ARG concentration downstream/near the source, but heterogeneous findings hindered making any sound conclusions. To quantify increases of ARG in the environment due to specific point sources, there is a need for studies that emphasize analytic or design control of confounding, and that provide effect measure estimates.
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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 | 0.008 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.000 | 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) | 0.004 | 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".