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BACKGROUND: Quality control is a system of validated procedures in which many samples, including active pharmaceutical ingredients and final products, are analyzed using standard or validated analytical methods. METHOD: Analytical methods used in analyzing active pharmaceutical ingredients or final products in the pharmaceutical industry can be methods registered in pharmacopeias and developed by the company itself. For this reason, published papers related to pharmaceutical analysis attract analysts and researchers' attention. In this study, pharmaceutical analysis and bioanalysis studies carried out between 2015 and 2023 were examined using Google Scholar, and the recent trends were determined for pharmaceutical analysis. Among the published papers performing conventional analytical techniques for pharmaceutical analysis, those applying UV-VIS spectrophotometry method were selected to predict a future perspective in this study. In addition to the data obtained, the current situation of the pharmaceutical industry was considered to correlate with the obtained data for pharmaceutical analysis. RESULTS: The results were presented with comparative tables and summarizing graphs. Interpreting the results allowed us to determine the trends that pharmaceutical analysis studies will lead in the future. This study can be helpful for researchers working on pharmaceutical analysis in both the industry and academia to predict future trends in pharmaceutical analysis. As a result of the literature research covering the dates 2015-2023, 56% of UV-VIS Spectrophotometric methods are used on pharmaceutical dosage forms, 27% are bulk, 16% are pure, 2% are biological materials, and 0.4% are herbal. Made from materials. Of these studies, 28% were conducted in the 200-240 nm range, 27% were conducted in the 240-300 nm range, and only 44% were conducted at >300 nm. Interpreting the results allowed us to determine the trends that pharmaceutical analysis studies will lead in the future. CONCLUSION: This study can be helpful for researchers working on pharmaceutical analysis in both the industry and academy side to predict future trends for pharmaceutical analysis.

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Academic departments, research clusters and evaluators analyze author and citation data to measure research impact and to support strategic planning. We created Scholar Metrics Scraper (SMS) to automate the retrieval of bibliometric data for a group of researchers. The project contains Jupyter notebooks that take a list of researchers as an input and exports a CSV file of citation metrics from Google Scholar (GS) to visualize the group's impact and collaboration. A series of graph outputs are also available. SMS is an open solution for automating the retrieval and visualization of citation data.

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The Journal Impact Factor (JIF) is a widely used metric for ranking journals based on the number of citations garnered by papers published over a specific timeframe. To assess the accuracy of JIF values, I compared citation counts for 30 of my own publications across six major bibliography databases: CrossRef, Web of Science, Publisher records, Google Scholar, PubMed and Scopus. The analysis revealed noteworthy variations in citation counts, ranging from 10% to over 50% between the lowest and highest citation counts. Google Scholar records the highest citation numbers, while PubMed reported the lowest. Notably, Web of Science, whose citation data are used in JIF calculations, tend to underestimate citation counts compared to other databases. These observations raise concerns about the accuracy of JIF calculation based on Web of Science's citation data. The real JIF values for most journals would differ from those annually reported by Clarivate's journal citation reports (JCR). These citation discrepancies underscore the importance of comprehensive data collection and the necessity to include additional citation sources. Not because a paper is cited in one journal rather than another should it have a less or more citation weight. Ultimately, one citation remains one citation, regardless of its origin. Clarivate Analytics may thus need to consider integrating all citation sources for more accurate JIF values. Alternatively, Google Scholar could potentially develop its own journal or citation impact based on its extensive journal citation records. However, while making adjustments to how the Journal Impact Factor is calculated can make it more mathematically precise, it doesn't address the fundamental biases built into the metric. Even with refinements, the Journal Impact Factor will remain skewed due to how it's defined and used.

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Los avances científicos han facilitado la difusión del conocimiento, encontrándose los más recientes hallazgos rápidamente en Internet, esto ha producido la migración de las revistas desde lo impreso a lo digital, pero este proceso no siempre se realiza adecuadamente, por lo que actualmente existen revistas, y en consecuencia sus publicaciones, que no se logran encontrar en los buscadores académicos, lo que se debe al uso de los softwares inadecuados o en su defecto a una mala configuración de los que se han implementado. En esta línea la recomendación es usar Open Journal System, un software diseñado para la publicación científica, pero varias revistas usan gestores de contenido como WordPress, por su facilidad de implementación y personalización aun cuando estos presenten limitaciones en el aspecto editorial. A continuación, se expone un método para la correcta indexación de revistas confeccionadas en WordPress en el buscador Google Scholar.

Scientific advances have facilitated the dissemination of knowledge, and the latest discoveries can be easily found on the Internet. This has produced the migration of journals from print to digital; however, this process is not always done properly since there are journals, and consequently their publications, which are not currently found in academic search engines due to the inappropriate use of software or otherwise to a misconfiguration of those that have been implemented. In this line, the recommendation is to use Open Journal System, a software designed for scientific publication; on the other hand, several journals use content manager systems such as WordPress because of its easy implementation and customization even when they present editorial constrains. The following is a method for the correct indexing of journals created in WordPress in the Google Scholar search engine.

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The under-representation of racial, sexual, and gender minorities in cancer clinical trials has long been a deficit in clinical cancer research. This review aims to survey current literature to determine the participation of minorities in the United States in lung cancer clinical trials and to find educational methods that have been studied and researched in order to improve patient clinical trial enrollment. A literature search of relevant articles published since 2015 was conducted using PubMed and Google Scholar. Clinical trials conducted in the United States from Clinicaltrials.gov were also collected to determine minority patient enrollment in lung cancer clinical trials. The results of the literature search yielded 6 relevant articles about racial minority representation in lung cancer clinical trials and one relevant article about LGBTQ+ minority representation in cancer clinical trials. Collectively, the literature highlighted the under-representation of racial minorities (such as Black, Hispanic, and American Indian) in clinical trials. Many articles showed that disparities in enrollment were less significant for Asian patients with lung cancer. However, many articles did not mention minorities like Middle Eastern/North Africans and failed to mention the lack of distinguishment of South Asian minorities from Pacific Asian minorities. The findings of this literature review support the idea that current lung cancer clinical trials lack representation of minority patient populations in the United States. The inclusion of racial, sexual, and gender diversity in clinical trial patient populations will aid providers in determining appropriate therapeutics and could potentially improve lung cancer outcomes. Future directions for improving diversity in lung cancer clinical trial enrollment include the utilization of various educational tools to increase minority patient participation in trials, the inclusion of detailed demographic data in cancer clinical trial analysis, and the recruitment of providers and research staff from various minorities to conduct cancer clinical trials.

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The strength of meta-analyses lies in the synthesis of data from multiple studies. Current guidelines require a thorough systematic search to maximize results, which usually includes searching multiple academic search systems (ASS). Google Scholar (GS) is considered a promising tool for searching the scientific literature. We aimed to determine whether GS is a valid and sufficient solitary data source for meta-analyses in the field of otolaryngology. Selected ENT-HNS journal was searched for meta-analyses published between 2010 and 2021 that adhered to the systematic reviews and meta-analyses guidelines and precisely followed the search algorithm. The latter was reproduced with GS, and the position of each enrolled study in each meta-analysis was determined. Ten meta-analyses were enrolled, the total number of search results ranged from 57 to 17,949. The number of GS search results was significantly greater than those of other ASS combinations (range 1,360-25,400, P = .006). The number of included papers for each meta-analysis ranged from 5 to 26. The position of all enrolled papers throughout GS searching was in the first 200 GS results in four of 10 meta-analyses. The reference lists of all included papers in the first 200 GS results identified 106 papers out of 108 (98%), while searching until the 500th GS output results identified 107 papers out of 108 papers (99%). GS can serve as a solitary ASS for systematic literature reviews and meta-analyses in the field of otolaryngology. Searching the first 500 or 200 results and including reference lists yields 99% and 98% coverage, respectively.

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One of the main concerns of researchers and institutions is how to assess the future performance of scholars and identify their potential to become successful scientists. In this study, we model scholarly success in terms of the probability of a scholar belonging to a group of highly impactful scholars as determined by their citation trajectory structures. To this end, we developed a new set of impact measures based on a scholar's citation trajectory structure (rather than on absolute citation or h-index rates), that show a stable trend and scale for highly impactful scholars, independent of their field of study, seniority and citation index. These measures were then incorporated as influence factors into the logistic regression models and used as features for probabilistic classifiers based on these models to identify the successful scholars in the heterogeneous corpus of 400 of most and least cited professors from two Israeli universities. From the practical point of view, the study may yield useful insights and serve as an aid in making promotion decisions by institutions, as well as a self-assessment tool for researchers who strive to increase their academic influence and become leaders in their field.

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BACKGROUND: To study the relation between social media mentions and academic citations for articles published in peer-reviewed orthodontic journals. METHODS: Articles published in early 2018 in seven peer-reviewed orthodontic journals were retrospectively analyzed in September 2022. Citation counts of the articles were evaluated using two databases: Google Scholar (GS) and Web of Science (WoS). The Altmetric Attention Score, Twitter, Facebook mentions, and Mendeley reads were tracked using the Altmetric Bookmarklet. The citation counts and social media mentions were correlated using Spearman rho. RESULTS: A total of 84 articles were identified during the initial search; 64 (76%) were original studies and systematic review articles and included in the analysis. A total of 38% of the articles had at least one mention on social media. Over the study period, the average number of citations of the articles mentioned on social media was higher than the non-mentioned articles for GS and WoS, respectively. Moreover, significant positive correlations existed between the Altmetric Attention Score and the number of citations in GS and WoS (rs = 0.31, P = 0.001 and rs = 0.26, P = 0.04). CONCLUSIONS: Social media mentions and citations of articles published in peer-reviewed orthodontic journals are correlated, with a clear difference in the number of citations in articles mentioned on social media versus those not mentioned, indicating possible increased reach of articles disseminated on social media.

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Google Académico (Google Scholar) es un buscador bibliográfico gratuito, especializado en recuperar documentos científicos de múlti-ples fuentes y en varias disciplinas. Por ejem-plo, artículos científicos, tesis, patentes o libros, además de identificar las citas que éstos han recibido, ayudando a conocer el impacto de las publicaciones.Cabe destacar que Google Académico se com-plementa con Google Books, Google Patents, Google Scholar Metrics, citas de Google y con los enlaces a bibliotecas. Estas características hacen de Google Académico una herramienta ideal para buscar en diversas fuentes desde un solo sitio, convirtiéndose así en un potente com-petidor de otros motores de búsqueda e índices de citas.(AU)

Google Scholar is a free bibliographic search engine, specialized in retrieving scientific docu-ments from multiple sources and in various dis-ciplines. For example, scientific articles, theses, patents or books, as well as identifying the cita-tions they have received, helping to determine the impact of the publications.It should be noted that Google Scholar is com-plemented by Google Books, Google Patents, Google Scholar Metrics, Google citations and links to libraries. These features make Google Scholar an ideal tool for searching diverse sources from a single site, making it a powerful competitor to other search engines and citation indexes.(AU)

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Despite the relative youth of bibliometric web platforms (Google Scholar was released in 2004), they play an increasingly significant role in the assessment of the impact of scholars and the research they produce. This scholarly essay provides a thorough review of the literature on bibliometric platforms, the extent to which they make available relevant manuscripts for inclusion in research, and their use for the assessment of scholarly work. We describe the metrics found on common bibliometric platforms, proposed metrics not commonly found in platforms, and how those metrics may differ based on scholar race and gender. We identify pitfalls of citation metrics present on bibliometric platforms. Finally, we identify areas for expansion of the research on bibliometric platforms and development of new metrics.

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Background and objective There is a paucity of information regarding the concordance of traditional metrics across publicly searchable databases and about the correlation between alternative and traditional metrics for neurosurgical authors. In this study, we aimed to assess the congruence between traditional metrics reported across Google Scholar (GS), Scopus (Sc), and ResearchGate (RG). We also aimed to establish the mathematical correlation between traditional metrics and alternative metrics provided by ResearchGate. Methods Author names listed on papers published in the Journal of Neurosurgery (JNS) in 2019 were collated. Traditional metrics [number of publications (NP), number of citations (NC), and author H-indices (AHi)] and alternative metrics (RG score, Research Interest score, etc. from RG and the GS i10-index) were also collected from publicly searchable author profiles. The concordance between the traditional metrics across the three databases was assessed using the intraclass correlation coefficient and Bland-Altman (BA) plots. The mathematical relation between the traditional and alternative metrics was analyzed. Results The AHi showed excellent agreement across the three databases studied. The level of agreement for NP and NC was good at lower median counts. At higher median counts, we found an increase in disagreement, especially for NP. The RG score, number of followers on RG, and Research Interest score independently predicted NC and AHi with a reasonable degree of accuracy. Conclusions A composite author-level matrix with AHi, RG score, Research Interest score, and the number of RG followers could be used to generate an "Impact Matrix" to describe the scholarly and real-world impact of a clinician's work.

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Background: To investigate the use of internet resources by surgeons for continuing professional development (CPD). Results: This cross-sectional study was carried out between July 1, 2021, to October 31, 2021, at the Department of Medicine, Health Sciences University Diskapi Yildirim Beyazit Training and Research Hospital, Ankara, Turkey, with participants from nine surgical specialties: General surgery, neurosurgery, orthopedics, urology, plastic surgery, ear-nose-throat surgery, cardiovascular surgery, ophthalmology, and anesthesiology. All study participants were asked to complete a questionnaire comprising 23 questions regarding their age, duration of work experience, appointment status, venue, and time spent on internet resources and preferred online resources for CPD purposes. In addition, participants were divided into two groups according to their appointment status: academic faculty and staff surgeons. Data analysis was performed using IBM SPSS Statistics version 17.0. The target population consisted of 216 specialists. The survey was completed by 204 (94.4%) surgical specialists. The majority of the specialists (n = 137, 67.2%) reported using the internet for work-related purposes every day. Daily time spent on internet resources was reported to be 30-60 min by 39.2% (n = 80) participants, whereas 52 (25.5%) reported spending less than 30 min. The participants wished to spend more time on internet resources. The majority of surgeons found the hospital and home equally effective in using the internet and preferred to engage alone. The mean age, English language level, usage of online resources, and the attitude score toward the perceived credibility and usefulness of e-resources were significantly higher in the academic faculty group than staff surgeons (p < 0.005). On the other hand, the use of Google/Google scholar was similar between the two groups (p = 0.192). Technical difficulties such as slow internet, need for website registration, and article fees were considered drawbacks for internet resources among all the participants. Conclusions: This study showed that most surgeons use internet resources daily for CPD and stated they would like to engage longer despite technical difficulties. Institutions should address these technical difficulties.

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BACKGROUND: The use of virtual reality in higher education show great potential to promote novel and innovative learning experiences. Until recently, virtual reality has mostly been used in technical higher education, but lately medical education programs have begun using virtual reality. Virtual reality for health professional education improves the knowledge and skills of health professionals compared with traditional or other digital education initiatives. However, the implementation of technology in higher education is slow because of barriers to technology use and innovative and successful practices are not shared. It is, therefore, of great interest to explore how virtual reality is implemented in higher health professional and continuing education. OBJECTIVE: The aim of this scoping review is to identify studies that reported implementation of virtual reality in higher health professional education, to identify barriers and facilitators for implementation, and to highlight research gaps in this area. METHODS: The scoping review will be conducted according to JBI Evidence Synthesis methodologies. CINAHL, the Academic Search Elite and Education Source electronic databases, and Google Scholar will be searched for studies published between 2017 and 2022. In addition, manual searching of key items, reference tracking, and citation tracking will be performed. Searches for white papers will also be manually conducted. All authors will independently extract data from full-text papers. We will use qualitative content analysis to abstract the findings. RESULTS: The literature searches were conducted in January and February 2022. The review is expected to be completed by fall 2022, after which time it will be submitted for publication. CONCLUSIONS: We anticipate that, from the review, we will be able to coordinate recommendations for and present the challenges of virtual reality initiatives in health professional education programs. We will present recommendations for future research. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/37222.

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OBJECTIVE: The use of machine learning (ML) has revolutionized every domain of medicine. Surgeons are now using ML models for disease detection and outcome prediction with high precision. ML-guided colorectal surgeries are more efficient than conventional surgical procedures. The primary aim of this paper is to provide an overview of the latest research on "ML in colorectal surgery", with its viable applications. METHODS: PubMed, Google Scholar, Medline, and Cochrane library were searched. RESULTS: After screening, 27 articles out of 172 were eventually included. Among all of the reviewed articles, those found to fit the criteria for inclusion had exclusively focused on ML in colorectal surgery, with justified applications. We identified existing applications of ML in colorectal surgery. Additionally, we discuss the benefits, risks, and safety issues. CONCLUSIONS: A better, more sustainable, and more efficient method, with useful applications, for ML in surgery is possible if we and data scientists work together to address the drawbacks of the current approach. Potential problems related to patients' perspectives also need to be resolved. The development of accurate technologies alone will not solve the problem of perceived unreliability from the patients' end. Confidence can only be developed within society if more research with precise results is carried out.

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Background Social media use among scholars and journals is growing and has augmented the academic impact of published articles in several areas of medicine. However, the influence of social media postings on academic citations of shoulder and elbow surgery publications is not known. In this study, we sought (1) to quantify the adoption of Twitter use for the dissemination of research publications by three prominent shoulder and elbow surgery journals and (2) to determine the correlation between Twitter mentions and academic citations in shoulder and elbow surgery publications. Methodology A total of 396 original research articles from three shoulder and elbow surgery journals (Journal of Shoulder and Elbow Surgery (JSES), Shoulder & Elbow, and JSES International) published in 2018 were assessed 34 to 45 months after print publication. For each article, the total number of Twitter mentions were obtained using Altmetric Bookmarklet and grouped into those tweeted by authors, an official outlet, or a third party. Article citation data was obtained using the Google Scholar search engine. Pearson correlation was used to determine the association between the number of Twitter mentions and citation count. Results Of all articles, 51% (202/396) had at least one Twitter mention. Of all Twitter mentions, 12.7% (367/2,879) occurred within the first week of online publication dates, while 51.5% (1,482/2,879) occurred between online and print publication dates. Articles mentioned on Twitter had 1.3-fold more Google Scholar citations (17.7 ± 15.2) than articles with no Twitter mentions (14.0 ± 15.7) (p = 0.017). The number of Twitter mentions had a weakly positive correlation with academic citation count (r = 0.25; p < 0.001). No significant difference in academic citation rates was found between articles tweeted by authors or official outlets when compared to articles tweeted by a third party only (p = 0.97 and p = 0.34, respectively). Conclusions Approximately half of shoulder and elbow surgery publications are shared on Twitter, with the majority of the activity occurring prior to their print publication date. The finding that tweeted articles have more academic citations within three years of release suggests that social media activity seems to amplify the academic impact of shoulder and elbow surgery publications.

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BACKGROUND: A study to reveal existing pedagogical content knowledge or technological pedagogical content knowledge frameworks and their effectiveness in teaching mathematics is crucial to inform the reader, teacher, and researcher. This review study intended to explore the trends of the pedagogical content knowledge (PCK) framework, how it has changed over time until the most recent version of technological and pedagogical content knowledge (TPACK) was developed, and their effectiveness in teaching mathematics. METHODS: We initially downloaded 273 articles from the first 30 Google Scholar pages and analyzed 229 journal articles. We got 24 frameworks from 64 journal articles since Shulman's first model in 1986. About 52 out of 229 were mathematics studies. Among these studies, we found that 18 studies have extensively investigated the use of identified frameworks. RESULTS: The frameworks were presented and descriptively discussed in chronological order. The empirical studies that compared the role of pedagogical content knowledge and technological pedagogical content knowledge models among classrooms with teachers who possess and do not possess such skills were demonstrated. CONCLUSIONS: The gap in empirical studies was identified, and further studies about the intervention of PCK and TPACK models were suggested to gain more insight into the mathematics classroom.

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Bibliometric analyses have been the primary form of examining and evaluating literature within a field of study. By focusing on citation count and source, researchers have been able to identify journal articles considered to be high impact in reach and relevance, branding them "citation classics" in a field. As time progresses, technology, methods, and metrics for conducting these analyses have improved, and although there have been several studies designed to identify citation classics and patterns of citations supporting them in school psychology literature, none have done so in an updated, comprehensive manner. To address these limitations, the current study aims to replicate and extend these works in three major ways: (a) including 11 primary school psychology journals in the search, (b) using three of the largest reference databases, and (c) collapsing results across these databases to accurately identify the most highly cited articles. The search yielded evidence of more than 12,000 articles accruing more than 500,000 citations. The 100 most highly cited articles were identified, and the majority were classified as explicative (n = 63) and quantitative (n = 70). Themes of bullying, burnout, and teacher-child relationships were the prominent focus. School psychology's citation classics tended to feature quantitative research and examine the relations between constructs, and several revealed a new category of citations classics: the methodological and statistical article.

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Introduction The Accreditation Council for Graduate Medical Education (ACGME) requires annual reporting of resident scholarly activities. However, this can be time-consuming for both residents and residency leadership and may not contain the most accurate or up-to-date information. This study sought to determine whether Google Scholar could adequately identify resident publications when compared with their ACGME self-report. Methods This was a cross-sectional study comparing resident Google Scholar publications with their ACGME self-reported data from 2018 to 2020. Manuscripts were only included if they were published prior to June 30, 2020, and while the participant was a resident at Rush University Medical Center. We did not count articles published prior to beginning residency. We independently collected data from the residents' self-report and Google Scholar profiles and compared the results. We assessed the overall concordance between data. When a discordant publication was identified, it was reviewed in full and discussed with the resident to ensure that it was correctly attributed to the resident. Data were presented primarily as descriptive statistics including percentages. Results Of 24 (96%) residents, 23 created Google Scholar profiles. There were 22 total publications. Google Scholar was concordant with self-report in 18 (78.3%) instances and discordant in five (21.7%) cases. In all five residents (n = 9 publications), the discordant publication was correctly identified by Google Scholar despite not being reported by the resident in their ACGME self-report. Conclusion We found that resident Google Scholar accounts resulted in the identification of publications that had not been previously reported on their ACGME self-report without missing any relevant publications.

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Rehabilitation Engineering is the use of engineering principles applied to rehabilitation, disability, and independent living. Google Scholar is a searchable resource that allows people from around the world to create profiles of their interests and collaborations, and it provides a means to search the broad scientific and technical literature. Google Scholar was used to identify the 150 most cited people who listed Rehabilitation Engineering in their profile. Research impact, characteristics, and areas of research of the most cited rehabilitation engineers were examined. Furthermore, gender and geographical differences in research metrics of the highest citied rehabilitation engineers were investigated. Consumer priorities in rehabilitation engineering were identified using a voice of consumer (VoC) survey and recent literature based on VoC studies. Gaps between research publication and activities and consumer priorities were identified to recommend seven areas of research with high demand and opportunity for growth and innovation. Implications.

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Providing ongoing care for a family member or loved one with special needs is challenging. It is estimated that about 21% of the adult population in the United States are providing unpaid care for loved ones at home. Of that group, 5.7% are caring for children with special needs. Special needs can range from developmental delay to the provision of complex medical treatments. Family caregivers take on numerous roles to coordinate activities of daily living, therapies, in-home services, school, and appointments. This is often a great source of stress on family caregivers. Providing the proper support for care to remain at home reduces the disruption to children and families' lives (Carter et al., 2012). A pilot project was developed for children with physical, cognitive, and/or developmental impairment. This project was coordinated by nursing and physical therapy faculty and their students. Parents as well as nursing and physical therapy students benefited mutually. Our preliminary responses from families indicated stress relief from caregiving and benefiting from the ability to talk to other parents in similar circumstances.

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