Statistical significance and effect sizes of differences among research universities at the level of nations and worldwide based on the leiden rankings

Geriatric Pain Protocol: Impact of Multimodal Pain Care for Elderly Orthopaedic Trauma Patients

Hip fractures are costly, and associated complications are the leading cause of injury-related deaths in persons 65 years or older. Pain medications in this population can be more potent, have a longer duration of action, and have serious side effects (Chau et al., 2008). Hip fractures are projected to reach 6.26 million worldwide by 2050 (Gullberg et al., 1997; Kannus et al., 1996). Morrison et al. (2003) report that uncontrolled pain leads to increased hospital length of stay (LOS), delayed physical therapy, and long-term functional impairment. The Geriatric Pain Protocol (GPP) is Cedars-Sinai's multimodal pain management solution, addressing the needs of older adult inpatients who have suffered fractures. Can the implementation of GPP reduce the morphine milligram equivalents (MMEs) used, LOS, and postoperative outcomes compared with non-GPP patients? Study participants included hip fracture patients admitted between February 1, 2019, and March 5, 2021; data were collected prospectively from electronic medical records. Inclusion criteria were patients 65 years or older with a hip fracture sustained from a ground-level fall and surgical candidate. Participants were divided into two categories: Geriatric Fracture Program (GFP) and non-GFP, with physician participation in the GFP being the differentiating factor. End points included postoperative pain, postoperative opioid utilization, LOS, complications, and 30-day readmission rates. The Mann-Whitney U test and Fisher's exact test were used for data analysis. Spearman's rank-based correlation coefficient was used to assess associations. The GPP decreased MME daily totals on Days 1 and 2 and improved pain management compared with non-GPP patients. The MMEs were lower in the GPP group than in the non-GPP group for both Postoperative Day 1 (POD1) (p = .007) and POD2 (p = .043); Numerical Rating Scale (NRS) Pain on POD1 was lower in the GPP group (vs. non-GPP, p = .013). There were no group differences in NRS POD2 Pain or complications (all ps > .1). The study sample (N = 453) had no significant difference between sex and LOS (all ps > .3). Although not statistically significant, the 30-day readmission rate trended lower in patients treated in accordance with the GPP. Use of the multimodal GPP reduced pain levels and MME totals for older adult hip fracture inpatients. More data are needed to evaluate the efficiency of the proposed protocol. Future studies should explore the possibilities of using the GPP across the geriatric orthopaedic patient care continuum.

A heuristic approach based on Leiden rankings to identify outliers: evidence from Italian universities in the European landscape

We propose an innovative use of the Leiden Rankings (LR) in institutional management. Although LR only consider research output of major universities reported in Web of Science (WOS) and share the limitations of other existing rankings, we show that they can be used as a base of a heuristic approach to identify “outlying” institutions that perform significantly below or above expectations. Our approach is a non-rigorous intuitive method (“heuristic”) because is affected by all the biases due to the technical choices and incompleteness that affect the LR but offers the possibility to discover interesting findings to be systematically verified later. We propose to use LR as a departure base on which to apply statistical analysis and network mapping to identify “outlier” institutions to be analyzed in detail as case studies. Outliers can inform and guide science policies about alternative options. Analyzing the publications of the Politecnico di Bari in more detail, we observe that “small teams” led by young and promising scholars can push the performance of a university up to the top of the LR. As argued by Moed (Applied evaluative informetrics. Springer International Publishing, Berlin, 2017a), supporting “emerging teams”, can provide an alternative to research support policies, adopted to encourage virtuous behaviours and best practices in research. The results obtained by this heuristic approach need further verification and systematic analysis but may stimulate further studies and insights on the topics of university rankings policy, institutional management, dynamics of teams, good research practice and alternative funding methods.

Are University Rankings Statistically Significant? A Comparison among Chinese Universities and with the USA

Purpose

Building on Leydesdorff, Bornmann, and Mingers (2019), we elaborate the differences between Tsinghua and Zhejiang University as an empirical example. We address the question of whether differences are statistically significant in the rankings of Chinese universities. We propose methods for measuring statistical significance among different universities within or among countries.

Design/methodology/approach

Based on z-testing and overlapping confidence intervals, and using data about 205 Chinese universities included in the Leiden Rankings 2020, we argue that three main groups of Chinese research universities can be distinguished (low, middle, and high).

Findings

When the sample of 205 Chinese universities is merged with the 197 US universities included in Leiden Rankings 2020, the results similarly indicate three main groups: low, middle, and high. Using this data (Leiden Rankings and Web of Science), the z-scores of the Chinese universities are significantly below those of the US universities albeit with some overlap.

Research limitations

We show empirically that differences in ranking may be due to changes in the data, the models, or the modeling effects on the data. The scientometric groupings are not always stable when we use different methods.

Practical implications

Differences among universities can be tested for their statistical significance. The statistics relativize the values of decimals in the rankings. One can operate with a scheme of low/middle/high in policy debates and leave the more fine-grained rankings of individual universities to operational management and local settings.

Originality/value

In the discussion about the rankings of universities, the question of whether differences are statistically significant, has, in our opinion, insufficiently been addressed in research evaluations.

Quantifying the publication preferences of leading research universities

Research universities have a strong devotion and advocacy for research in their core academic mission. This is why they are widely recognized for their excellence in research which make them take the most renowned positions in the different worldwide university leagues. In order to examine the uniqueness of this group of universities we analyze the scientific production of a sample of them in a 5 year period of time. On the one hand, we analyze their preferences in research measured with the relative percentage of publications in the different subject areas, and on the other hand, we calculate the similarity between them in research preferences. In order to select a set of research universities, we studied the leading university rankings of Shanghai, QS, Leiden, and Times Higher Education (THE). Although the four rankings own well established and developed methodologies and hold great prestige, we choose to use THE because data were readily available for doing the study we had in mind. Having done that, we selected the twenty academic institutions ranked with the highest score in the last edition of THE World University Rankings 2020 and to contrast their impact, we also, we compared them with the twenty institutions with the lowest score in this ranking. At the same time, we extracted publication data from Scopus database for each university and we applied bibliometrics indicators from Elsevier's SciVal. We applied the statistical techniques cosine similarity and agglomerative hierarchical clustering analysis to examine and compare affinities in research preferences among them. Moreover, a cluster analysis through VOSviewer was done to classify the total scientific production in the four major fields (health sciences, physical sciences, life sciences and social sciences). As expected, the results showed that top universities have strong research profiles, becoming the leaders in the world in those areas and cosine similarity pointed out that some are more affine among them than others. The results provide clues for enhancing existing collaboration, defining and re-directing lines of research, and seeking for new partnerships to face the current pandemic to find was to tackle down the covid-19 outbreak.