# Bibliometric Analysis of Medical Waste Research Using Python-Driven Algorithm
## 2 Materials and Methods  
In this investigation, bibliometric data from the Web of Science (WOS) database was utilized. WOS was selected as the database for a number of important reasons: the database offers complete record entries with fewer missing abstracts, allowing robust text analysis necessary for our keyword co-occurrence and thematic evolution investigations, offers comprehensive coverage of multidisciplinary journals relevant to medical waste management, guaranteeing representation across medical, environmental, and engineering domains. Using a single comprehensive database avoided the methodological challenges associated with cross-database integration. To ensure the comprehensive retrieval of relevant material, a targeted search was conducted using specific keywords targeting the research topic. The literature search was conducted in February 2024, covering the complete timespan available in the Web of Science database (1975-2024). This comprehensive time horizon was selected to capture the entire historical development of medical waste management research, with the earliest relevant publication identified from 1975. Our search strategy employed the following Boolean query: ALL=((\"Medical waste\" OR \"Pharmaceutical waste\" OR \"waste pharmaceutical\" OR \"waste medical\" OR \"hospital waste\") AND (hospital OR management OR \"sustainable development\" OR \"circular economy\" OR sorting OR biomedical OR hazardous OR \"non-hazardous\" OR infectious OR surgical OR segregation OR disposal OR recycling OR separation OR production OR collection OR transportation OR treatment OR radioactive OR chemical OR sharp OR general OR sterilization OR micro saving OR carbonization OR bio converted OR incineration OR pyrolysis OR gasification OR irradiation OR immobilization OR \"reverse polymerization\" OR policy OR legislation OR physician OR doctor OR pharmacist) yielded a total of 3515 documents, to which the following exclusion criteria was applied: only documents writ-ten in English, articles and reviews. In this search strategy, the field tag 'ALL=' instructs the database to search across all searchable fields of the records, including titles, abstracts, keywords, and other metadata. The Boolean operator 'OR' broadens the search by retrieving documents containing any of the specified terms, while 'AND' narrows the search by requiring documents to contain terms from both sets of criteria, ensuring all retrieved documents address some aspect of medical waste alongside at least one of the specified management or contextual terms. After applying these filters, our final dataset comprised 3025 documents focused on medical waste management. Figure 1 illustrates the systematic search algorithm and document selection workflow applied in this bibliometric analysis, detailing each step of query improvement, screening, and final selection to ensure relevance and accuracy. For reducing the number of irrelevant results, the initial wide query was subjected to a series of modifications, including the incorporation of inclusion/exclusion criteria and the application of exact phrase matching (using the quotation mark operator on keywords). This resulted in a notable reduction in the number of false positives. The algorithm and document selection procedure ensured the generation of a targeted and precise dataset for the bibliometric study. The documents were exported in a tab-delimited file format using the WOS's native 'Export' function. To ensure the inclusion of all relevant data, the 'Full Record and Cited References' option was selected. The bibliometric analysis was conducted using the VOSviewer (version 1.6.20), Bibliometrix programs via the Biblioship web interface, and Microsoft Excel [28]. These tools facilitate the analysis of scientific data extracted from databases such as WOS and Scopus, thereby enabling the identification of trends, collaborative networks, and thematic evolution [29; 30].