Global research trends on the links between disinfection by-products and cancer: mapping knowledge landscapes and visualization analysis
Bibliographic record
Abstract
Although disinfection has been successful in ensuring microbiological safety, there are growing concerns regarding the potential carcinogenic effects linked to exposure to disinfection by-products (DBPs). Research on the formation, toxicity, and prevalence of DBPs is still limited for many compounds. Consequently, this study seeks to utilize bibliometric analysis of literature on the associations between DBPs and cancer to elucidate the current research landscape and highlight areas of focus for future studies. A total of 1,045 publications were identified through an extensive search of the Scopus database spanning the years 1976-2023. The United States led with 345 publications (33.0%), followed by China with 236 publications (22.6%) and Canada with 69 publications (6.6%). The identified hot topics were categorized into three clusters: (i) mechanisms of DBPs formation resulting from the use of various disinfectants to treat water contaminated with emerging pollutants; (ii) the identification of different types of cancers associated with DBPs; and (iii) research on the genotoxicity and toxicity evaluation associated with DBPs. It would be wise to develop interdisciplinary research within international horizons. Moreover, the drinking and wastewater treatment standards need revision to include DBP limits.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.051 | 0.067 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".