Virtual Laboratories- A historical review and bibliometric analysis of the past three decades.

Online and virtual teaching-learning has been a panacea that most educational institutions adopted from the dire need created by COVID-19. We provide a comprehensive bibliometric study of 9523 publications on virtual laboratories in higher education covering the years 1991 to 2021. Influential bibliometrics such as publications and citations, productive countries, contributing institutions, funders, journals, authors, and bibliographic couplings were studied using the Scientific Procedures and Rationales for Systematic Literature Reviews (SPAR-4-SLR) protocol. A new metric to complement citations called Field Weighted Citation Impact was introduced that considers the differences in research behavior across disciplines. Findings show that 72% of the research work was published between 2011-and 2021, most likely due to digitalization, with the highest number of publications in 2020-2021 highlighting the impact of the pandemic. Top contributing institutions were from the developed economies of Spain, Germany, and the United States. The citation impact from publications with international co-authors is the highest, highlighting the importance of co-authoring papers with different countries. For the first time, Altmetrics in the context of virtual labs were studied though a very low correlation was observed between citations and Altmetrics Attention Score. Still, the overall percentage of publications with attention showed linear growth. Our work also highlights that virtual laboratory could play a significant role in achieving the United Nations Sustainable Development Goals, specifically SDG4-Quality Education, which largely remains under-addressed.

Adoption of online proctored examinations by university students during COVID-19: Innovation diffusion study.

COVID-19 global pandemic pushed a large number of higher educational institutions to use Online Proctored Exams (OPE) because of government-imposed lockdowns. Treating OPE as an educational technology innovation, we apply the diffusion of innovation theory in predicting factors affecting its adoption by university students which we believe is the first of its kind research study. The study presented here reviews OPE, its types, architecture, challenges, and prospects and then focuses on the student adoption experience at a large, multi-campus higher educational institution. We have used the fine-grained Aspect Level Sentiment Analysis to check the university students' attitudes towards the Online Proctored Exams. We then used linguistic features to extract the aspect terms present in the feedback comments which showed that 55% of university students having a positive attitude towards OPE. Results of our study show that innovation characteristics such as relative advantage, compatibility, ease of use, trialability, and observability were found to be positively related to acceptance of OPE.

Personalized Health Monitoring System for Managing Well-Being in Rural Areas.

Rural India lacks easy access to health practitioners and medical centers, depending instead on community health workers. In these areas, common ailments that are easy to manage with medicines, often lead to medical escalations and even fatalities due to lack of awareness and delayed diagnosis. The introduction of wearable health devices has made it easier to monitor health conditions and to connect doctors and patients in urban areas. However, existing initiatives have not succeeded in providing adequate health monitoring to rural and low-literate patients, as current methods are expensive, require consistent connectivity and expect literate users. Our design considerations address these concerns by providing low-cost medical devices connected to a low-cost health platform, along with personalized guidance based on patient physiological parameters in local languages, and alerts to medical practitioners in case of emergencies. This patient-centric integrated healthcare system is designed to manage the overall health of villagers with real-time health monitoring of patients, to offer guidance on preventive care, and to increase health awareness and self-monitoring at an affordable price. This personalized health monitoring system addresses the health-related needs in remote and rural areas by (1) empowering health workers in monitoring of basic health conditions for rural patients in order to prevent escalations, (2) personalized feedback regarding nutrition, exercise, diet, preventive Ayurveda care and yoga postures based on vital parameters and (3) reporting of patient data to the patient's health center with emergency alerts to doctor and patient. The system supports community health workers in the diagnostic procedure, management, and reporting of rural patients, and functions well even with only intermittent access to Internet.

Complementing Neurophysiology Education for Developing Countries via Cost-Effective Virtual Labs: Case Studies and Classroom Scenarios.

Classroom-level neuroscience experiments vary from detailed protocols involving chemical, physiological and imaging techniques to computer-based modeling. The application of Information and Communication Technology (ICT) is revolutionizing the current laboratory scenario in terms of active learning especially for distance education cases. Virtual web-based labs are an asset to educational institutions confronting economic issues in maintaining equipment, facilities and other conditions needed for good laboratory practice. To enhance education, we developed virtual laboratories in neuroscience and explored their first-level use in (Indian) University education in the context of developing countries. Besides using interactive animations and remotely-triggered experimental devices, a detailed mathematical simulator was implemented on a web-based software platform. In this study, we focused on the perceptions of technology adoption for a virtual neurophysiology laboratory as a new pedagogy tool for complementing college laboratory experience. The study analyses the effect of virtual labs on users assessing the relationship between cognitive, social and teaching presence. Combining feedback from learners and teachers, the study suggests enhanced motivation for students and improved teaching experience for instructors.

Applications of artificial intelligence and machine learning in the financial services industry: A bibliometric review.

This bibliometric review examines the research state of artificial intelligence (AI) and machine learning (ML) applications in the Banking, Financial Services, and Insurance (BFSI) sector. The study focuses on Scopus-indexed articles to identify key research clusters. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol, 39,498 articles were screened, resulting in 1045 articles meeting the inclusion criteria. N-gram analysis identified 177 unique terms in the article titles and abstracts. Co-occurrence analysis revealed nine distinct clusters covering fintech, risk management, anti-money laundering, and actuarial science, among others. These clusters offer a comprehensive overview of the multifaceted research landscape. The identified clusters can guide future research and inform study design. Policymakers, researchers, and practitioners in the BFSI sector can benefit from the study's findings, which identify research gaps and opportunities. This study contributes to the growing literature on bibliometrics, providing insights into AI and ML applications in the BFSI sector. The findings have practical implications, advancing our understanding of AI and ML's role in benefiting academia and industry.

Evaluating human resources management literacy: A performance analysis of ChatGPT and bard.

This study presents a comprehensive analysis comparing the literacy levels of two Generative Artificial Intelligence (GAI) tools, ChatGPT and Bard, using a dataset of 134 questions from the Human Resources (HR) domain. The generated responses are evaluated for accuracy, relevance, and clarity. We find that ChatGPT outperforms Bard in overall accuracy (84.3% vs. 82.8%). This difference in performance suggests that ChatGPT could serve as a robotic advisor in transactional HR roles. In contrast, Bard may possess additional safeguards against misuse in the HR function, making it less capable of generating responses to certain types of questions. Statistical tests reveal that although the two systems differ in their mean accuracy, relevance, and clarity of the responses, the observed differences are not always statistically significant, implying that both tools may be more complementary than competitive. The Pearson correlation coefficients further support this by showing weak to non-existent relationships in performance metrics between the two tools. Confirmation queries don't improve ChatGPT or Bard's response accuracy. The study thus contributes to emerging research on the utility of GAI tools in Human Resources Management and suggests that involving certified HR professionals in the design phase could enhance underlying language model performance.

Diagnosis Unreliability of ChatGPT for Journal Evaluation.

Purpose: Academic and other researchers have limited tools with which to address the current proliferation of predatory and hijacked journals. These journals can have negative effects on science, research funding, and the dissemination of information. As most predatory and hijacked journals are not error free, this study used ChatGPT, an artificial intelligence (AI) technology tool, to conduct an evaluation of journal quality. Methods: Predatory and hijacked journals were analyzed for reliability using ChatGPT, and the reliability of result have been discussed. Results: It shows that ChatGPT is an unreliable tool for journal quality evaluation for both hijacked and predatory journals. Conclusion: To show how to address this gap, an early trial version of Journal Checker Chatbot has been developed and is discussed as an alternative chatbot that can assist researchers in detecting hijacked journals.

Top 100 highly cited sustainability researchers.

The announcement of the UN Sustainable Development Goals (SDGs) provided a fresh direction to sustainability research that spans different disciplines. Consequently, scholarly databases made available the mapping of research publications to different SDGs, unleashing many opportunities for analysis. In this work, the top 100 Highly Cited Sustainability Researchers (HCSRs) and information related to them, such as the institutions they belong to, the type of these institutions, the geographical diversity of these researchers, and gender representation patterns, are analyzed. Also, from their publications, their publication pattern, including (i) the least and most researched SDGs, (ii) their Open Access publishing pattern, (iii) their collaboration pattern (iv) the pattern of their research impact, are analyzed. The most sought thematic areas of their research, top journals in which they publish, important research categories handled by these journals, etc., are also investigated. The most significant contribution of these researchers and their recent contributions are also discussed. The data indicates a significant disparity in research focus among the top 100 HCSRs, with most concentrating on "Good Health and Well Being," "Zero Hunger," and "Quality Education," while notably fewer researchers focus on "Decent Work and Economic Growth" and "No Poverty," underscoring the need for a more balanced research agenda across all SDGs. The study reveals that the United States, China, and the United Kingdom are the leading contributors to the top 100 HCSRs, suggesting that these countries are predominant in global sustainability research output, while nations like Iran and Saudi Arabia also make notable, albeit smaller, contributions. The institutional affiliations of HCSRs show a significant imbalance, with only 16 from private institutions compared to 84 from public ones. Specifically, it shows that out of the top 100 researchers, 93 are men, while only 7 are women. The analysis of authorship in publications by HCSRs reveals a tendency towards middle and last author positions, underscoring their collaborative and leadership roles within the research community. All these analyses can inform academia, industry, and policymakers about the most significant developments in research regarding SDGs.

Fake news research trends, linkages to generative artificial intelligence and sustainable development goals.

In the digital age, where information is a cornerstone for decision-making, social media's not-so-regulated environment has intensified the prevalence of fake news, with significant implications for both individuals and societies. This study employs a bibliometric analysis of a large corpus of 9678 publications spanning 2013-2022 to scrutinize the evolution of fake news research, identifying leading authors, institutions, and nations. Three thematic clusters emerge: Disinformation in social media, COVID-19-induced infodemics, and techno-scientific advancements in auto-detection. This work introduces three novel contributions: 1) a pioneering mapping of fake news research to Sustainable Development Goals (SDGs), indicating its influence on areas like health (SDG 3), peace (SDG 16), and industry (SDG 9); 2) the utilization of Prominence percentile metrics to discern critical and economically prioritized research areas, such as misinformation and object detection in deep learning; and 3) an evaluation of generative AI's role in the propagation and realism of fake news, raising pressing ethical concerns. These contributions collectively provide a comprehensive overview of the current state and future trajectories of fake news research, offering valuable insights for academia, policymakers, and industry.

ChatGPT: Literate or intelligent about UN sustainable development goals?

Generative AI tools, such as ChatGPT, are progressively transforming numerous sectors, demonstrating a capacity to impact human life dramatically. This research seeks to evaluate the UN Sustainable Development Goals (SDGs) literacy of ChatGPT, which is crucial for diverse stakeholders involved in SDG-related policies. Experimental outcomes from two widely used Sustainability Assessment tests-the UN SDG Fitness Test and Sustainability Literacy Test (SULITEST) - suggest that ChatGPT exhibits high SDG literacy, yet its comprehensive SDG intelligence needs further exploration. The Fitness Test gauges eight vital competencies across introductory, intermediate, and advanced levels. Accurate mapping of these to the test questions is essential for partial evaluation of SDG intelligence. To assess SDG intelligence, the questions from both tests were mapped to 17 SDGs and eight cross-cutting SDG core competencies, but both test questionnaires were found to be insufficient. SULITEST could satisfactorily map only 5 out of 8 competencies, whereas the Fitness Test managed to map 6 out of 8. Regarding the coverage of the Fitness Test and SULITEST, their mapping to the 17 SDGs, both tests fell short. Most SDGs were underrepresented in both instruments, with certain SDGs not represented at all. Consequently, both tools proved ineffective in assessing SDG intelligence through SDG coverage. The study recommends future versions of ChatGPT to enhance competencies such as collaboration, critical thinking, systems thinking, and others to achieve the SDGs. It concludes that while AI models like ChatGPT hold considerable potential in sustainable development, their usage must be approached carefully, considering current limitations and ethical implications.

Transparency in research: An analysis of ChatGPT usage acknowledgment by authors across disciplines and geographies.

This investigation systematically reviews the recognition of generative AI tools, particularly ChatGPT, in scholarly literature. Utilizing 1,226 publications from the Dimensions database, ranging from November 2022 to July 2023, the research scrutinizes temporal trends and distribution across disciplines and regions. U.S.-based authors lead in acknowledgments, with notable contributions from China and India. Predominantly, Biomedical and Clinical Sciences, as well as Information and Computing Sciences, are engaging with these AI tools. Publications like "The Lancet Digital Health" and platforms such as "bioRxiv" are recurrent venues for such acknowledgments, highlighting AI's growing impact on research dissemination. The analysis is confined to the Dimensions database, thus potentially overlooking other sources and grey literature. Additionally, the study abstains from examining the acknowledgments' quality or ethical considerations. Findings are beneficial for stakeholders, providing a basis for policy and scholarly discourse on ethical AI use in academia. This study represents the inaugural comprehensive empirical assessment of AI acknowledgment patterns in academic contexts, addressing a previously unexplored aspect of scholarly communication.

Detection of fake papers in the era of artificial intelligence.

OBJECTIVES: Paper mills, companies that write scientific papers and gain acceptance for them, then sell authorships of these papers, present a key challenge in medicine and other healthcare fields. This challenge is becoming more acute with artificial intelligence (AI), where AI writes the manuscripts and then the paper mills sell the authorships of these papers. The aim of the current research is to provide a method for detecting fake papers. METHODS: The method reported in this article uses a machine learning approach to create decision trees to identify fake papers. The data were collected from Web of Science and multiple journals in various fields. RESULTS: The article presents a method to identify fake papers based on the results of decision trees. Use of this method in a case study indicated its effectiveness in identifying a fake paper. CONCLUSIONS: This method to identify fake papers is applicable for authors, editors, and publishers across fields to investigate a single paper or to conduct an analysis of a group of manuscripts. Clinicians and others can use this method to evaluate articles they find in a search to ensure they are not fake articles and instead report actual research that was peer reviewed prior to publication in a journal.

Darkweb research: Past, present, and future trends and mapping to sustainable development goals.

The Darkweb, part of the deep web, can be accessed only through specialized computer software and used for illegal activities such as cybercrime, drug trafficking, and exploitation. Technological advancements like Tor, bitcoin, and cryptocurrencies allow criminals to carry out these activities anonymously, leading to increased use of the Darkweb. At the same time, computers have become an integral part of our daily lives, shaping our behavior, and influencing how we interact with each other and the world. This work carries out the bibliometric study on the research conducted on Darkweb over the last decade. The findings illustrate that most research on Darkweb can be clustered into four areas based on keyword co-occurrence analysis: (i) network security, malware, and cyber-attacks, (ii) cybercrime, data privacy, and cryptography, (iii) machine learning, social media, and artificial intelligence, and (iv) drug trafficking, cryptomarket. National Science Foundation from the United States is the top funder. Darkweb activities interfere with the Sustainable Development Goals (SDG) laid forth by the United Nations to promote peace and sustainability for current and future generations. SDG 16 (Peace, Justice, and Strong Institutions) has the highest number of publications and citations but has an inverse relationship with Darkweb, as the latter undermines the former. This study highlights the need for further research in bitcoin, blockchain, IoT, NLP, cryptocurrencies, phishing and cybercrime, botnets and malware, digital forensics, and electronic crime countermeasures about the Darkweb. The study further elucidates the multi-dimensional nature of the Darkweb, emphasizing the intricate relationship between technology, psychology, and geopolitics. This comprehensive understanding serves as a cornerstone for evolving effective countermeasures and calls for an interdisciplinary research approach. The study also delves into the psychological motivations driving individuals towards illegal activities on the Darkweb, highlighting the urgency for targeted interventions to promote pro-social online behavior.

Ayurveda research: Emerging trends and mapping to sustainable development goals.

Ayurveda is India's prominent traditional medical system. The World Health Organization has stated the need for more evidence and data from conventional medicine methods to inform policymakers, regulatory bodies, healthcare stakeholders, and the public about its safe, effective, and equitable use. This study aims to provide a comprehensive analysis of the emerging trends in Ayurveda research, mapping research to the UN Sustainable Development Goals (SDG) and examining the impact of COVID-19. Using bibliometric methods, the researchers analyzed a total of 11,773 publications between 1993 and 2022 to understand the temporal evolution of publications, open-access publications, patterns of author collaboration, top-performing countries, and co-citation networks. The keyword co-occurrence analysis identifies networks of concentrated studies on Ayurveda research themes relating to the four clusters, Alternative and Traditional Medicine, Bioactive Compounds and Biological Activities, Analytical Techniques and Herbal Standardization, and Herbal Medicines and Immunomodulation, reflecting the diverse research areas within Ayurveda. The last cluster included research related to the SARS-CoV-2 virus, suggesting research on herbal approaches to immune modulation in the context of COVID-19. The most prominent SDG among these research themes was Good Health and Well-being (SDG 3), emphasizing the potential of natural products and traditional medicine in promoting holistic health and combating antibiotic resistance.

Mapping sustainability reporting research with the UN's sustainable development goal.

Non-financial reporting (NFR) has become crucial to corporate sustainability strategies as companies demonstrate their commitment to the environmental, social, and governance actions outlined in the United Nations Sustainable Development Goals (SDGs) Agenda 2030. Among the various mandatory NFR initiatives, Sustainability Reporting (SR) has emerged as a widely adopted practice by companies worldwide. A gap that the study addresses is the theoretical perspectives on SR in the context of SDG. Then we conduct a bibliometric and science mapping analysis of research trends on SR and precisely map SR research to SDGs which is also a gap in the current literature. We find an exponential increase in the number of publications and citations on SR, particularly after 2015, which coincides with increased public awareness and scrutiny of the SDGs. At the country level, Australia leads with a total of 13 SDGs, followed by the UK, Spain, and Italy, which each address 12 SDGs. Emerging economies such as Indonesia, Malaysia, and India have also increased their contributions since 2019. A keyword co-occurrence analysis identified three main clusters: stakeholder engagement, corporate governance, and accountability; sustainable development goals and climate change; and sustainability reporting and global reporting initiatives. All three clusters had highly cited publications related to SDG 8 (decent work), SDG 9 (industry innovation), and SDG 12 (responsible consumption). This highlights the interdisciplinary nature of SR and its relevance to multiple SDGs. The study is distinctive in that we utilized social network analysis to examine the SDG network based on SR publications, which also affirmed the centrality of SDG 9 and 12. We utilized the prominence percentile, which indicates the momentum of a particular topic, to identify future topics in SR that align with the SDGs. These include cause-related marketing, environmentally preferable purchasing decisions, environmental management systems, education for sustainability, and green computing.

Mapping autism's research landscape: trends in autism screening and its alignment with sustainable development goals.

Introduction: Autism Spectrum Disorder is a complex neurodevelopmental syndrome that profoundly affects social interactions, communication, and sensory perception. The research traced the evolution of autism research from 2011-2022, specifically focusing on the screening and diagnosis of children and students. Methods: Through an analysis of 12,262 publications using the PRISMA framework, bibliographic coupling, science mapping, and citation analysis, this study illuminates the growth trajectory of ASD research and significant disparities in diagnosis and services. Results: The study indicates an increasing trend in autism research, with a strong representation of female authorship. Open Access journals show a higher average citation impact compared to their closed counterparts. A keyword co-occurrence analysis revealed four central research themes: Child Development and Support Systems, Early Identification and Intervention, Prevalence and Etiology, and Mental Health. The pandemic's onset has prioritized research areas like mental health, telehealth, and service accessibility. Discussion: Recommendations on a global level stress the importance of developing timely biological markers for ASD, amplifying Disability Inclusion research, and personalizing mental health services to bridge these critical service gaps. These strategies, underpinned by interdisciplinary collaboration and telehealth innovation, particularly in low-resource settings, can offer a roadmap for inclusive, context-sensitive interventions at local levels that directly support SDG3's aim for health and well-being for all.

A Toolkit for Detecting Fallacious Calls for Papers from Potential Predatory Journals.

Purpose: Flattering emails are crucial in tempting authors to submit papers to predatory journals. Although there is ample literature regarding the questionable practices of predatory journals, the nature and detection of spam emails need more attention. Current research provides insight into fallacious calls for papers from potential predatory journals and develops a toolkit in this regard. Methods: In this study, we analyzed three datasets of calls for papers from potential predatory journals and legitimate journals using a text mining approach and R programming language. Results: Overall, most potential predatory journals use similar language and templates in their calls for papers. Importantly, these journals praise themselves in glorious terms involving positive words that may be rarely seen in emails from legitimate journals. Based on these findings, we developed a lexicon for detecting unsolicited calls for papers from potential predatory journals. Conclusion: We conclude that calls for papers from potential predatory journals and legitimate journals are different, and it can help to distinguish them. By providing an educational plan and easily usable tools, we can deal with predatory journals better than previously.

Early Osteoarthritis Questionnaire (EOAQ): a tool to assess knee osteoarthritis at initial stage.

Background: Early stage of osteoarthritis (OA) is characterized by joint stiffness and pain as well as by subclinical structural changes that may affect cartilage, synovium, and bone. At the moment, the lack of a validated definition of early osteoarthritis (EOA) does not allow to make an early diagnosis and adopt a therapeutic strategy to slow disease progression. Also, no questionnaires are available to evaluate the early stage, and therefore this remains an unmet need. Objective: Therefore, the purpose of the technical experts panel (TEP) of 'International Symposium of intra-articular treatment' (ISIAT) was to create a specific questionnaire to evaluate and monitor the follow-up and clinical progress of patients affected by early knee OA. Design: The items for the Early Osteoarthritis Questionnaire (EOAQ) were identified according to the following steps: items generation, items reduction, and pre-test submission. Methods: During the first step, literature has been reviewed and a comprehensive list of items about pain and function in knee EOA was drafted. Then, during the ISIAT (5th edition 2019), the draft has been discussed by the board, which reformulated, deleted, or subdivided some of the items. After the ISIAT symposium, the draft was submitted to 24 subjects affected by knee OA. A score based on the importance and the frequency was created and the items with a score ⩾0.75 were selected. After intermediate evaluation made by a sample of patients, the second and final version of the questionnaire EOAQ was submitted to the whole board for final analysis and acceptance in a second meeting (29 January 2021). Results: After an exhaustive elaboration, the final version of the questionnaire contains two domains (Clinical Features and Patients Reported Outcome) with respectively 2 and 9 questions, for a total of 11 questions. Questions mainly explored the fields of early symptoms and patients reported outcomes. Marginally, the need of the symptoms treatment and the use of painkillers were investigated. Conclusions: Adoption of diagnostic criteria of early OA is strongly encouraged and a specific questionnaire for the whole management of the clinical features and patients' outcome might really improve the evolution of OA in the early stages of the disease, when the treatment is expected to be more effective.

Pattern of contraceptive use, determinants and fertility intentions among tribal women in Kerala, India: a cross-sectional study.

OBJECTIVES: To assess the pattern of contraceptive use and its determinants, knowledge regarding contraceptives including oral contraceptive pills and fertility intentions among tribal women in the reproductive age group. DESIGN: Community-based cross-sectional study. SETTING: Community development blocks in a predominantly tribal district of Wayanad in Kerala, India. PARTICIPANTS: Women in the reproductive age group (15-49 years) from the tribal groups in the district numbering 2495. OUTCOME MEASURES: PRIMARY: Prevalence of contraceptive use and its determinants. SECONDARY: Knowledge regarding contraceptives in general, oral contraceptives and fertility intentions. RESULTS: The mean age of the study participants was 30.8 years (SD=9.8) and belonged to various tribal groups such as Paniya (59.2%), Kurichiyar (13.6%) and Adiya (10.9%). Current use of contraceptive was reported by about a fourth, 658 (26.4%) (95% CI 27.9 to 24.9) of women. Following logistic regression, belonging to Paniya tribe (adjusted OR (aOR) 2.67, 95% CI 1.49 to 4.77; p<0.001) and age at menarche >13 years (aOR 1.69, 95% CI 1.14 to 2.52; p<0.009) had significantly higher use of contraceptives whereas social vulnerability as indicated by staying in a kutcha house had a lesser likelihood of use of contraceptive (aOR 0.55, 95% CI 0.31 to 0.95; p<0.03). Oral contraceptive use was low (4.8%) among this population and no abuse was observed.Less than half (47%) of the respondents had an above average knowledge on contraception. Multivariable logistic regression indicated that above average knowledge was 2.2 times more likely with higher education (95% CI 1.2 to 3.9), lesser among those who desired more than two children (aOR 0.59; 95% CI 0.38 to 0.94; p<0.02).Two children per family was the preferred choice for 1060 (42.5%) women. No gender bias in favour of the male child was observed. CONCLUSION: Awareness and use of contraceptives are poor though the fertility is not commensurately high. Along with developing targeted responses to contraceptive use among Indigenous people with indigenous data, awareness also requires attention. Ethnographic studies are also necessary to determine the differences in contraceptive use including traditional methods among the various Indigenous groups.

The Birmingham hip resurfacing prosthesis: an independent single surgeon's experience at 7-year follow-up.

An independent single surgeon's 7-year experience with Birmingham hip resurfacing is presented. The study also involved investigation of the significance of pedestal sign in patients requiring revision. A consecutive 117 hips in 101 patients (59 male and 42 female patients) operated on by the senior author (VGJ) were assessed at a mean follow-up of 7 years (range, 5-9.4 years). Mean age at surgery was 54 years (range, 20-74 years). Seventy-three hips had a preoperative diagnosis of primary osteoarthritis, and secondary osteoarthritis was seen in 44 hips. Failure was defined as revision for any reason. Revision of the femoral component alone was undertaken in 8 hips (6.8%): 5 within first year for periprosthetic fracture neck of femur and in 3 hips after 5 years of follow-up. In 2 patients who were known to have osteonecrosis of the femoral head preoperatively, the femoral component progressively collapsed into varus after 5 years of follow-up. Pedestal sign was the earliest radiologic sign noted in these 2 patients and progressed rapidly within 1 year on serial radiographs well before the onset of clinical symptoms. Kaplan-Meier survival with revision as end point at 7 years was 91.5% (95% confidence interval, 97.6%-85.4%).