Systematic Review: Impact of point sources on antibiotic‐resistant bacteria in the natural environment
Bibliographic record
Abstract
Point sources such as wastewater treatment plants and agricultural facilities may have a role in the dissemination of antibiotic-resistant bacteria (ARB) and antibiotic resistance genes (ARG). To analyse the evidence for increases in ARB in the natural environment associated with these point sources of ARB and ARG, we conducted a systematic review. We evaluated 5,247 records retrieved through database searches, including both studies that ascertained ARG and ARB outcomes. All studies were subjected to a screening process to assess relevance to the question and methodology to address our review question. A risk of bias assessment was conducted upon the final pool of studies included in the review. This article summarizes the evidence only for those studies with ARB outcomes (n = 47). Thirty-five studies were at high (n = 11) or at unclear (n = 24) risk of bias in the estimation of source effects due to lack of information and/or failure to control for confounders. Statistical analysis was used in ten studies, of which one assessed the effect of multiple sources using modelling approaches; none reported effect measures. Most studies reported higher ARB prevalence or concentration downstream/near the source. However, this evidence was primarily descriptive and it could not be concluded that there is a clear impact of point sources on increases in ARB in the environment. To quantify increases in ARB in the environment due to specific point sources, there is a need for studies that stress study design, control of biases and analytical tools to 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 | 0.019 | 0.109 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.011 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".