# Bibliometric Analysis of Medical Waste Research Using Python-Driven Algorithm
## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Study Title/** & **Study Objectives** & **Database** & **Time Span** & **No. of Papers** & **Tools Used** & **Analytical Depth** & **Limitations** & **Ref. ** \\
**First Author** & **Study Objectives** & **Database** & **Time Span** & **No. of Papers** & **Tools Used** & **Analytical Depth** & **Limitations** & **Ref. ** \\ \hline \hline
\begin{tabular}{c} Mapping of Research \\ Output on Medical Waste \\ Management: A Mapping \\  
The present study sets itself apart through several innovative and methodological advances. It spans one of the longest analysis periods to date (i.e., 1975-2024) and relies on the largest and most refined dataset (i.e., 3025 documents) among comparable research. This search strategy represents one of the most complex keyword algorithms in the field, incorporating 41 unique terms to ensure the comprehensive retrieval of relevant literature on medical waste management. A key differentiator is the integration of a custom-built Python-based thesaurus generator, which, to our knowledge, is being used for the first time in bibliometric analyses targeting the field of MW, which applies TF-IDF, cosine similarity, and semantic clustering to standardize keywords. To ensure rigorous and reproducible analysis, we systematically validated our Python-based thesaurus tool. The clustering precision rate was assessed by having three researchers independently evaluate 300 randomly selected terms, with a success threshold requiring agreement on at least 270 keywords. The tool successfully met this threshold, confirming its effectiveness in identifying synonymous relationships, eliminating redundancies, and consolidating semantically equivalent terms into coherent groups. By combining multiple analytical tools, including VOSviewer, Bibliometric, and Excel, the study offers robust visualizations such as co-authorship networks, journal citation maps, trend topic trajectories, and thematic evolution diagrams. Special attention was also given to enhancing figure readability through improved legends, color schemes, and clarification of technical terms. In contrast, previous bibliometric studies targeting various aspects of MW have typically relied on Scopus and smaller datasets, lacking the co-word analysis to discern trending topics and thematic evolution in the field of MW [25]. A subsequent study also incorporated fewer types of analyses, with methodologies more narrowly focused on literature reviews rather than comprehensive data-driven assessments [6]. Furthermore, one of these studies targets a simple bibliometric analysis without engaging in critical comparative discussions with other works or providing in-depth evaluations of relevant articles, which would help in understanding the evolving trends in publications both thematically and methodologically over time [53]. Additionally, another study has concentrated solely on COVID-19-related MW using smaller datasets, offering a more limited scope compared to broader, long-term explorations of the field [26]. Most studies recognize the COVID-19 pandemic as a catalyst for intensified waste-related research, yet the present work distinguishes itself through methodological precision and a forward-looking, sustainability-centered analytical frame. While bibliometric studies offer valuable insights, it is important to consider their inherent strengths and limitations. One strength is their capacity to analyze large quantities of research data, thereby providing an overview of trends and patterns across extensive fields of study. This, however, can also constitute a limitation, as the considerable number of articles makes it not feasible to verify each one manually, which may result in the inclusion of false positives in the data. A key strength of the present bibliometric studies is the application of custom Python code, such as a thesaurus generation algorithm.