Microsoft Academic Research Articles

Mapping Open Access Publishing Trends in Central Universities of North East India: A Scientometric Approach

ABSTRACT The study analyses open access publications across the 10 central universities of North East India, focusing on publication trends, disciplinary distribution, citation impact, collaboration patterns, and open access licensing patterns. It compiles scholarly outputs from the OpenAlex API, integrating various data sources such as Microsoft Academic Graph, ORCID, Crossref, and Unpaywall. A total of 28,832 publications from 2004–2023 were collected and analyzed using statistical methods. The findings reveal a positive trend in research output across the represented universities, with an increasing emphasis on OA publications. However, the proportion of OA publications remains relatively low compared to closed access. Citation impact varies among universities, reflecting differences in research quality and visibility. Open repositories such as SSRN, Research Square, and Zenodo are crucial in promoting OA publishing. The study also reveals common usage of licenses such as CC-BY and CC-BY-NC-ND, with variations across universities. The impact on libraries is also discussed, emphasizing the role of libraries in promoting OA, supporting researchers, and contributing to scholarly communication advancement. This study provides valuable insights for policymakers, academic administrators, and researchers seeking to promote a culture of openness, collaboration, and innovation in scholarly communication within the region, country, and at the global level.

Exploring How Acquired Diversity of Teams Influences the Innovation Diffusion in Artificial Intelligence

ABSTRACTThis study explores innovation diffusion in Artificial Intelligence (AI) over the past 70 years, focusing on the impact of acquired diversity(including discipline diversity and topic diversity) of teams. We obtained 906,065 AI papers with 18,357,761 citations written by 1,316,936 authors between 1950 and 2015 from the Microsoft Academic Graph. Using citation analysis, we examine how AI innovations spread and the influence of the acquired diversity of teams on this process. We measure innovation diffusion from four aspects: innovation speed, innovation diffusion power, innovation diffusion breadth, and innovation diffusion breadth diversity. Our findings show that: (1) AI papers published before and after 2000 demonstrate distinct diffusion patterns. AI papers published before 2000 show a bimodal citation trend, while those released after 2000 present only a single peak in citations. (2) The first citation interval can be reduced by 2.35 years before 2000 and by 0.27 years after 2000 by enhancing topic diversity. (3) Both discipline diversity and topic diversity effectively promote innovation diffusion power, innovation diffusion breadth, and innovation diffusion breadth diversity in the field of AI.

The importance of monitoring neurological manifestations in rehabilitation after radical hysterectomy

Background. Hysterectomy is currently one of the most frequently performed surgical operations in obstetrics and gynecology, second only to cesarean section. However, such intervention is often associated with the risk of complications, including neurological ones. Neurological consequences, due to their characteristics, go beyond physical manifestations and exert psycho-emotional pressure on patients.Objective: to determine the need for monitoring neurological manifestations in women undergoing rehabilitation after hysterectomy.Material and methods. The search and selection of studies in Russian and English languages were conducted according to PRISMA recommendations in scientific databases and electronic libraries: PubMed/MEDLINE, ScienceDirect, Google Scholar, Microsoft Academic, DOAJ, eLibrary, CiberLeninka. In total, 2569 publications were revealed; after subsequent screening 57 articles were selected, which were devoted to the study of neurological adverse effects of hysterectomy and met all the screening criteria.Results. Among all the neurological complications mentioned, the most common is chronic postoperative pain. It is detected in 30% of cases. At the same time, in the vast majority of cases, such pains are rare and are tolerated as mild discomfort. However, there is no unambiguous understanding of the nature of this complication. There is a theory about the essential role of estrogen production and substance P ratio in pain manifestations. Thus, a decrease in estrogen production with unchanged synthesis of substance P significantly reduces the pain threshold in patients, which is the cause of the observed chronic pain. A certain contribution to the formation of this neurological complication is made by residual ovarian syndrome, which is associated with incomplete removal of the ovaries during ovariectomy, traumatic neuroma and mononervopathy. Damage to the nerves or their plexuses in the pelvic area can cause intestinal and urinary disorders, leading to a decrease in the quality of life of patients. Excessive anxiety and depression are additional risk factors for the development of chronic pain after hysterectomy, and the accompanying postoperative changes often have a physical manifestation, for example, in the form of insomnia, restless legs syndrome and sexual dysfunction.Conclusion. Hysterectomy is certainly an effective way to treat a number of gynecological diseases, but it often causes complications that require correction of the rehabilitation program. Neurological negative manifestations need special monitoring, since the depressive state of patients may lead to chronicity of pathological processes.

Evolutions of semantic consistency in research topic via contextualized word embedding

Topic evolution has been studied extensively in the field of the science of science. This study first analyzes topic evolution pattern from topics’ semantic consistency in the semantic vector space, and explore its possible causes. Specifically, we extract papers in the computer science field from Microsoft Academic Graph as our dataset. We propose a novel method for encoding a topic with numerous Contextualized Word Embeddings (CWE), in which the title and abstract fields of papers studying the topic is taken as its context. Subsequently, we employ three geometric metrics to analyze topics’ semantic consistency over time, from which the influence of the anisotropy of CWE is excluded. The K-Means clustering algorithm is employed to identify four general evolution patterns of semantic consistency, that is, semantic consistency increases (IM), decreases (DM), increases first and then decreases (Inverted U-shape), and decreases first and then increases (U-shape). We also find that research methods tend to show DM and U-shape, but research questions tend to be IM and Inverted U-shape. Finally, we further utilize the regression analysis to explore whether and, if so, how a series of key features of a topic affect its semantic consistency. Importantly, semantic consistency of a topic varies inversely with the semantic similarity between the topic and other topics. Overall, this study sheds light on the evolution law of topics, and helps researchers to understand these patterns from a geometric perspective.

Bridging the gap in author names: building an enhanced author name dataset for biomedical literature system.

Author name incompleteness, referring to only first initial available instead of full first name, is a long-standing problem in MEDLINE and has a negative impact on biomedical literature systems. The purpose of this study is to create an Enhanced Author Names (EAN) dataset for MEDLINE that maximizes the number of complete author names. The EAN dataset is built based on a large-scale name comparison and restoration with author names collected from multiple literature databases such as MEDLINE, Microsoft Academic Graph, and Semantic Scholar. We assess the impact of EAN on biomedical literature systems by conducting comparative and statistical analyses between EAN and MEDLINE's author names dataset (MAN) on 2 important tasks, author name search and author name disambiguation. Evaluation results show that EAN improves the number of full author names in MEDLINE from 69.73 million to 110.9 million. EAN not only restores a substantial number of abbreviated names prior to the year 2002 when the NLM changed its author name indexing policy but also improves the availability of full author names in articles published afterward. The evaluation of the author name search and author name disambiguation tasks reveal that EAN is able to significantly enhance both tasks compared to MAN. The extensive coverage of full names in EAN suggests that the name incompleteness issue can be largely mitigated. This has significant implications for the development of an improved biomedical literature system. EAN is available at https://zenodo.org/record/10251358, and an updated version is available at https://zenodo.org/records/10663234.

Cost-effectiveness of Microsoft Academic Graph with machine learning for automated study identification in a living map of coronavirus disease 2019 (COVID-19) research.

Identifying new, eligible studies for integration into living systematic reviews and maps usually relies on conventional Boolean updating searches of multiple databases and manual processing of the updated results. Automated searches of one, comprehensive, continuously updated source, with adjunctive machine learning, could enable more efficient searching, selection and prioritisation workflows for updating (living) reviews and maps, though research is needed to establish this. Microsoft Academic Graph (MAG) is a potentially comprehensive single source which also contains metadata that can be used in machine learning to help efficiently identify eligible studies. This study sought to establish whether: (a) MAG was a sufficiently sensitive single source to maintain our living map of COVID-19 research; and (b) eligible records could be identified with an acceptably high level of specificity. We conducted an eight-arm cost-effectiveness analysis to assess the costs, recall and precision of semi-automated workflows, incorporating MAG with adjunctive machine learning, for continually updating our living map. Resource use data (time use) were collected from information specialists and other researchers involved in map production. Our systematic review software, EPPI-Reviewer, was adapted to incorporate MAG and associated machine learning workflows, and also used to collect data on recall, precision, and manual screening workload. The semi-automated MAG-enabled workflow dominated conventional workflows in both the base case and sensitivity analyses. At one month our MAG-enabled workflow with machine learning, active learning and fixed screening targets identified 469 additional, eligible articles for inclusion in our living map, and cost £3,179 GBP per week less, compared with conventional methods relying on Boolean searches of Medline and Embase. We were able to increase recall and coverage of a large living map, whilst reducing its production costs. This finding is likely to be transferrable to OpenAlex, MAG's successor database platform.

Meta-analysis Influence of Integrated Mind Mapping Inquiry Based Learning Model on Student Problem Solving Skills

A This study aims to determine the effect of inquiry-based learning model integrated mind mapping on students' problem-solving skills. This type of research is quantitative research with meta-analysis methods. Data comes from international journals or proceedings published 2015-2023. Data search through ScienceDirect, Springer, ProQuest, ERIC and Taylor of Francis databases. Research should be selected for data in meta-analyses. The inclusion criteria are that research must be experimental methods or quasi-experiments, research indexed by Scopus, Sinta, Web of Science, Microsoft Academic and EBSCO, Research has an experimental class inquiry learning model integrated mind mapping and conventional model control class, research has a number of samples (N), correlation values (r), t, and F and a sample consisting of 1021 participants. Data analysis with the help of JASP to determine the value of effect size, heterogeneity test and publication bias. The results showed a positive influence of minde mapping-based inquiry learning model on students' problem solving skills ( z = 7.019; p < 0.001; 95% CI [0.61; 1.26]. This finding shows that the influence of mind mapping-based inquiry learning models is included in the very high category (rRE = 1.31).

Decoding the writing styles of disciplines: A large-scale quantitative analysis

Disciplinary writing style stems from the practice of science, reflecting the scientific culture. This study aims to explore the differences and evolution of scientific writing styles from the perspective of disciplines. A large-scale quantitative analysis was conducted over 14 million abstracts from the Microsoft Academic Graph (MAG) database across eight soft and hard disciplines. Represented by a comprehensive set of 14 symbolic, lexical, syntactic, structural, and readability features, the evolution of disciplinary writing styles was analyzed over 30 years. Interpretable machine learning methods were performed to test the discernibility of writing styles across disciplines and disclose their linguistic differences. Our findings reveal the linguistic features of soft disciplines (Art, Philosophy, and Sociology) and Mathematics generally keep stabilized, and a general trend of increasing linguistic complexity was observed for Biology, Chemistry, Computer Science, and Psychology. The good performance of the pairwise writing style classifiers indicates a well discriminability of the writing styles between disciplines. A correlation between the performance of classifiers and the distance between disciplines was identified. The feature contribution analysis using SHapley Additive exPlanations (SHAP) and Kendall's Tau rank correlation revealed the detailed commonalities and disparities in disciplines’ linguistic features. This study provides profound insights into the understanding of scientific writing and norms, which further helps develop useful tools for academic text analysis, foster interdisciplinary communication, and assist educators to construct discipline-specific writing guidance.

Completeness degree of publication metadata in eight free-access scholarly databases

Abstract The main objective of this study is to compare the amount of metadata and the completeness degree of research publications in new academic databases. Using a quantitative approach, we selected a random Crossref sample of more than 115,000 records, which was then searched in seven databases (Dimensions, Google Scholar, Microsoft Academic, OpenAlex, Scilit, Semantic Scholar, and The Lens). Seven characteristics were analyzed (abstract, access, bibliographic info, document type, publication date, language, and identifiers), to observe fields that describe this information, the completeness rate of these fields, and the agreement among databases. The results show that academic search engines (Google Scholar, Microsoft Academic, and Semantic Scholar) gather less information and have a low degree of completeness. Conversely, third-party databases (Dimensions, OpenAlex, Scilit, and The Lens) have more metadata quality and a higher completeness rate. We conclude that academic search engines lack the ability to retrieve reliable descriptive data by crawling the web, and the main problem of third-party databases is the loss of information derived from integrating different sources.

Higher-order structures of local collaboration networks are associated with individual scientific productivity

The prevalence of teamwork in contemporary science has raised new questions about collaboration networks and the potential impact on research outcomes. Previous studies primarily focused on pairwise interactions between scientists when constructing collaboration networks, potentially overlooking group interactions among scientists. In this study, we introduce a higher-order network representation using algebraic topology to capture multi-agent interactions, i.e., simplicial complexes. Our main objective is to investigate the influence of higher-order structures in local collaboration networks on the productivity of the focal scientist. Leveraging a dataset comprising more than 3.7 million scientists from the Microsoft Academic Graph, we uncover several intriguing findings. Firstly, we observe an inverted U-shaped relationship between the number of disconnected components in the local collaboration network and scientific productivity. Secondly, there is a positive association between the presence of higher-order loops and individual scientific productivity, indicating the intriguing role of higher-order structures in advancing science. Thirdly, these effects hold across various scientific domains and scientists with different impacts, suggesting strong generalizability of our findings. The findings highlight the role of higher-order loops in shaping the development of individual scientists, thus may have implications for nurturing scientific talent and promoting innovative breakthroughs.

Characterizing structure of cross-disciplinary impact of global disciplines: A perspective of the Hierarchy of Science

Abstract Purpose Interdisciplinary fields have become the driving force of modern science and a significant source of scientific innovation. However, there is still a paucity of analysis about the essential characteristics of disciplines’ cross-disciplinary impact. Design/methodology/approach In this study, we define cross-disciplinary impact on one discipline as its impact to other disciplines, and refer to a three-dimensional framework of variety-balance-disparity to characterize the structure of cross-disciplinary impact. The variety of cross-disciplinary impact of the discipline was defined as the proportion of the high cross-disciplinary impact publications, and the balance and disparity of cross-disciplinary impact were measured as well. To demonstrate the cross-disciplinary impact of the disciplines in science, we chose Microsoft Academic Graph (MAG) as the data source, and investigated the relationship between disciplines’ cross-disciplinary impact and their positions in the Hierarchy of Science (HOS). Findings Analytical results show that there is a significant correlation between the ranking of cross-disciplinary impact and the HOS structure, and that the discipline exerts a greater cross-disciplinary impact on its neighboring disciplines. Several bibliometric features that measure the hardness of a discipline, including the number of references, the number of cited disciplines, the citation distribution, and the Price index have a significant positive effect on the variety of cross-disciplinary impact. The number of references, the number of cited disciplines, and the citation distribution have significant positive and negative effects on balance and disparity, respectively. It is concluded that the less hard the discipline, the greater the cross-disciplinary impact, the higher balance and the lower disparity of cross-disciplinary impact. Research limitations In the empirical analysis of HOS, we only included five broad disciplines. This study also has some biases caused by the data source and applied regression models. Practical implications This study contributes to the formulation of discipline-specific policies and promotes the growth of interdisciplinary research, as well as offering fresh insights for predicting the cross-disciplinary impact of disciplines. Originality/value This study provides a new perspective to properly understand the mechanisms of cross-disciplinary impact and disciplinary integration.

Perspectives of Women with Intellectual Disabilities Regarding Affective Sexual Relationships: a Systematic Literature Review

IntroductionDespite the recognition of the right to sexual identity and expression for all people, individuals with intellectual disabilities continue to face significant restrictions in these aspects of their lives. The prevailing myths and stereotypes surrounding their sexual-affective relationships place people with intellectual disabilities—especially women—in a position of great vulnerability.MethodsA systematic literature review was conducted, examining the content of the following databases: BASE, Dialnet, ERIC, La Referencia, Microsoft Academic, Redalyc, REDIB, Redined, Science Direct, Scopus, Web of Science, and World Wide Science. A total of 178 potentially relevant articles were identified, out of which 11 met the eligibility criteria. Finally, the themes covered in the articles were identified and analyzed.ResultsThe results obtained were classified into six categories: perceptions regarding sex and sexuality, experiences of abuse, views and experiences of relationships between couples, the LGTBIQ + community, knowledge about contraception and related experiences, and, finally, beliefs regarding gender scripts.ConclusionsThis study offers valuable insights into the knowledge (or lack thereof) and beliefs regarding sex and sexuality, experiences of abuse among women with intellectual disabilities, the lack of independence and external intervention, and the influence of traditional gender scripts. Furthermore, this research also establishes a theoretical foundation for future support resources and initiatives, as well as social and public implications.

Meta Analysis: The Effect of Emotional Qoutient (EQ) on Students' Mathematics Learning Outcomes

This research aims to determine the influence of emotional qoutient (EQ) on students' mathematics learning outcomes. The type of research used is meta-analysis which is a quantitative research method. Data collection was carried out by searching for relevant articles according to the topic being studied on internet network sites such as Google Scholar, Research gate One Search, Open Knowledge Map, Search, and Microsoft Academic with the keyword influence of emotional quotient on students' mathematics learning outcomes. The sampling technique uses purposive sampling technique. The samples analyzed are research reports that meet predetermined criteria. The instrument used is a coding sheet (coding data). Data analysis was carried out by looking for the effect size value using the summary effect formula. Based on the analysis carried out, it was found that there was an influence of emotional quotient on the mathematics learning outcomes of moderate students with an average effect size of 0.778 and was included in the category. Thus, educators or stakeholders in the world of education can design more holistic learning programs by including EQ aspects to increase the effectiveness of mathematics learning and student learning outcomes.

Transformers in medical image segmentation: a narrative review.

Transformers, which have been widely recognized as state-of-the-art tools in natural language processing (NLP), have also come to be recognized for their value in computer vision tasks. With this increasing popularity, they have also been extensively researched in the more complex medical imaging domain. The associated developments have resulted in transformers being on par with sought-after convolution neural networks, particularly for medical image segmentation. Methods combining both types of networks have proven to be especially successful in capturing local and global contexts, thereby significantly boosting their performances in various segmentation problems. Motivated by this success, we have attempted to survey the consequential research focused on innovative transformer networks, specifically those designed to cater to medical image segmentation in an efficient manner. Databases like Google Scholar, arxiv, ResearchGate, Microsoft Academic, and Semantic Scholar have been utilized to find recent developments in this field. Specifically, research in the English language from 2021 to 2023 was considered. In this survey, we look into the different types of architectures and attention mechanisms that uniquely improve performance and the structures that are in place to handle complex medical data. Through this survey, we summarize the popular and unconventional transformer-based research as seen through different key angles and analyze quantitatively the strategies that have proven more advanced. We have also attempted to discern existing gaps and challenges within current research, notably highlighting the deficiency of annotated medical data for precise deep learning model training. Furthermore, potential future directions for enhancing transformers' utility in healthcare are outlined, encompassing strategies such as transfer learning and exploiting foundation models for specialized medical image segmentation.

Meta Analisis: Pengaruh Media Pembelajaran terhadap Hasil Belajar Matematika Peserta Didik

Improving the quality of mathematics learning is an urgent need in education. One factor that plays a crucial role in the learning process is the use of learning media. This research aims to find out (1) the type of media that influences overall mathematics learning, (2) the summary effect size of the application of learning media that influences students' mathematics learning outcomes, and (3) whether the application of learning media influences students' mathematics learning outcomes. This type of research is meta-analysis, which is a study that uses data from previous research to be analyzed systematically and quantitatively in order to obtain comprehensive conclusions regarding the results of the research being studied. Data collection was carried out by searching for articles that were relevant to the topic being studied on internet network sites such as Google Scholar, Research Gate, One Search, Open Knowledge Map, Eric, IPI Search, and Microsoft Academic, using the keywords the influence of learning media on student learning outcomes. The samples analyzed are research reports that meet the predetermined sample criteria. The instruments used are data collection sheets, screening sheets, and feasibility sheets. Data analysis techniques use descriptive and inferential statistics. This research found that the most influential media was print media; the summary effect size was 0.636, which is within the criteria for a large high effect size, and the hypothesis test results showed that learning media had a significant effect on student learning outcomes.

Monodisciplinary collaboration disrupts science more than multidisciplinary collaboration

AbstractCollaboration across disciplines is a critical form of scientific collaboration to solve complex problems and make innovative contributions. This study focuses on the association between multidisciplinary collaboration measured by coauthorship in publications and the disruption of publications measured by the Disruption (D) index. We used authors' affiliations as a proxy of the disciplines to which they belong and categorized an article into multidisciplinary collaboration or monodisciplinary collaboration. The D index quantifies the extent to which a study disrupts its predecessors. We selected 13 journals that publish articles in six disciplines from the Microsoft Academic Graph (MAG) database and then constructed regression models with fixed effects and estimated the relationship between the variables. The findings show that articles with monodisciplinary collaboration are more disruptive than those with multidisciplinary collaboration. Furthermore, we uncovered the mechanism of how monodisciplinary collaboration disrupts science more than multidisciplinary collaboration by exploring the references of the sampled publications.

Why are these publications missing? Uncovering the reasons behind the exclusion of documents in free‐access scholarly databases

AbstractThis study analyses the coverage of seven free‐access bibliographic databases (Crossref, Dimensions—non‐subscription version, Google Scholar, Lens, Microsoft Academic, Scilit, and Semantic Scholar) to identify the potential reasons that might cause the exclusion of scholarly documents and how they could influence coverage. To do this, 116 k randomly selected bibliographic records from Crossref were used as a baseline. API endpoints and web scraping were used to query each database. The results show that coverage differences are mainly caused by the way each service builds their databases. While classic bibliographic databases ingest almost the exact same content from Crossref (Lens and Scilit miss 0.1% and 0.2% of the records, respectively), academic search engines present lower coverage (Google Scholar does not find: 9.8%, Semantic Scholar: 10%, and Microsoft Academic: 12%). Coverage differences are mainly attributed to external factors, such as web accessibility and robot exclusion policies (39.2%–46%), and internal requirements that exclude secondary content (6.5%–11.6%). In the case of Dimensions, the only classic bibliographic database with the lowest coverage (7.6%), internal selection criteria such as the indexation of full books instead of book chapters (65%) and the exclusion of secondary content (15%) are the main motives of missing publications.

The expansion of team size in library and information science (LIS): Is bigger always better?

The increasing prevalence of large research teams in contemporary science has prompted an investigation into the trends of team size in library and information science (LIS). In this study, we analysed 103,299 LIS publications written by 129,560 unique authors between 2000 and 2020 sourced from the Microsoft Academic Graph (MAG) data sets. We conducted a temporal analysis from multiple dimensions, including journal quartiles, research topics and researchers with varying impact levels. In addition, we employed two multivariate linear regression models – with and without author fixed effects – to scrutinise the relationship between team size and publication impact. Our findings reveal continuous growth in LIS team size. Notably, publications in higher-quartile journals tend to have larger teams; the team size of technical topic publications is generally larger than that of theoretical topic publications; and researchers with a higher h-index are able to assemble larger teams. Although we observed that co-authored papers have a higher average citation impact than single-authored papers, the overall positive impact of team expansion on citation growth is not always significant within the common LIS team size range (three to six authors). Our research suggests that indiscriminately increasing the size of a team may not be a prudent decision.

Document Co-citation Analysis using Concept Lattice

Document Co-citation Analysis (DCA) is a method to identify and analyze the relationships between co-cited documents. In this paper, we attempt to use concept lattice for DCA. Concept lattice is a graph structure given in Formal Concept Analysis (FCA), a branch of mathematics based on the concept and its hierarchy. The experiments are conducted on an extensive repository of citations extracted from DBLP, ACM, MAG (Microsoft Academic Graph), and other sources, having a total of 5,354,309 papers and 48,227,950 citation relationships. In this paper, it is established that the concept lattice supports DCA and helps to identify a set of co-cited documents and their co-citation strength. It also provides navigation to reflect the subset-superset relationship of the co-citations. Further, the concept lattice helps identify the hierarchy among the documents and answers the most relevant queries related to DCA.

Neuropathic Pain and Lumbar Spinal Column Injuries: A Chronic Perspective

Background: Neuropathic pain commonly occurs as a consequence of spinal cord injuries, manifesting as persistent and often debilitating pain sensations resulting from damage or dysfunction of the nervous system within the spinal column. This systematic review aimed to analyze existing guidelines on rehabilitation interventions for neuropathic pain and clarify their significance. Methodology: A comprehensive search of literature was performed using the databases Google Scholar, Microsoft Academic, and PubMed. The inclusion criteria encompassed research articles written in Greek, English, and French languages, while articles that were not directly relevant to the subject of investigation were excluded. Results: A systematic review of published guidelines on rehabilitation interventions for neuropathic pain to elucidate their significance. While some interventions, such as TENS therapy, physiotherapy, and psychological interventions, have been proposed, the core approach to managing neuropathic pain lies in the implementation of comprehensive multidisciplinary interventions that address all the various aspects affected by this chronic and highly disabling condition. It is worth noting the limited literature examining the role of rehabilitation specifically in patients with neuropathic pain, with a focus on individual interventions solely targeting the treatment of neuropathic pain. Conclusions: Neuropathic pain has significant effects not only on the injuries of the lumbar spine, but also on subsequent functions. Therefore, the involvement of a specialist focused on managing and restoring functional impairments is necessary. This role can be fulfilled by a physical therapist who specializes in addressing the functional issues associated with the patient’s condition. Physiotherapists should employ a multiprofessional and multidisciplinary approach, collaborating closely with various healthcare professionals involved in the patient’s management to ensure optimal pain management.