What are the effective solutions to control the dissemination of antibiotic resistance in the environment? A systematic review protocol
Bibliographic record
Abstract
Abstract Background Antibiotic treatments are indispensable for human and animal health. However, the heavy usage of antibiotics has led to the emergence of resistance. Antibiotic residues, antibiotic-resistant bacteria and genes are introduced into the terrestrial and aquatic environments via application of human and animal wastes. The emergence and the spread of antibiotic resistance in environmental reservoirs (i.e., soil, water, wildlife) threatens the efficacy of all antibiotics. Therefore, there is an urgent need to determine what effective solutions exist to minimize the dissemination of antibiotic resistance in the environment. The aim of this article is to describe the protocol of a systematic review of the literature considering these solutions. Methods The primary questions addressed by the systematic review protocol are: how antibiotic resistance in the environment is impacted by changes in practice concerning (i) the use of antibiotics, (ii) the management of wastes or (iii) the management of the natural compartment. Bibliographic searches will be made in eleven publication databases as well as in specialist databases. Grey literature will also be searched. Articles will be screened regarding the inclusion and exclusion criteria at title, abstract and full-text levels. Studies where a causal relationship between the intervention and the outcome is made will be retained. After critical appraisal, data from the selected articles will be extracted and saved in a database validated by the expert panel. Study quality will be assessed by critical appraisal. Data will be compiled into a qualitative synthesis. If data availability and quality allow it, a quantitative synthesis will be carried out.
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 | 0.166 | 0.172 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.018 | 0.015 |
| Bibliometrics | 0.019 | 0.015 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.008 | 0.007 |
| Research integrity | 0.012 | 0.006 |
| Insufficient payload (model declined to judge) | 0.051 | 0.011 |
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".