bluuebunny/crossref_metadata_2025_split huggingface dataset:
Psychology of Science
Authors: Krauss Alexander 
Published: November 2024

AbstractWhen we do science and acquire knowledge about the world, our mind also faces psychological biases. We face limited mental resources, time constraints and incomplete information, so we use simplified heuristics like rules of thumb or shortcuts when reasoning. We for example often rely on existing assumptions and evidence when formulating a hypothesis or applying a given scientific method rather than questioning and testing them every time. In general, our mind has largely evolved to be able to absorb and process a limited amount of information and then make quick assumptions, decisions and conclusions based on that (incomplete) information. We think fast, are habit-based and use heuristics most of the time, which can result in unconscious biases. Our reasoning is influenced not only by psychological biases but also by personality traits.
Sociology of Science
Authors: Krauss Alexander 
Published: November 2024

AbstractIf the cognitive abilities of children at birth a few hundred or even thousand years ago and today are likely not very different, what can explain the large differences in their theories of the world as adults? While our mind’s methodological abilities to observe, solve problems and experiment are a precondition of science and we have used them more systematically over time, changes in broader demographic, social and economic factors have fostered developing vast knowledge and methods. There were hundreds of scientists a few centuries ago. Today, there are about nine million full-time scientists worldwide. Demographic growth and complex social organisation have been crucial for the scientific community to grow and for greater collaboration, cumulative knowledge and methodological development. Doing science has become an increasingly complex social activity. We are also not disinterested and detached observers of reality but guided by institutions, shaped by scientific norms and motivated by values that can influence our research.
Economics of Science
Authors: Krauss Alexander 
Published: November 2024

AbstractWe can foster science through economies of scale, a reward system, science policy and targeted research funding. As society becomes more productive, diversified and efficient in providing goods and services, more individuals can dedicate themselves to scientific activities. Science can function like an economy: just as a growing and more specialised labour force generally develops more diversified goods and technologies, a growing and more specialised scientific community generally develops more diversified knowledge and methods. Economies of scale and agglomeration facilitate greater division of labour across and within scientific fields—and thus greater methodological diversity and knowledge. Public institutions also help plan, finance and manage how we produce, distribute and use knowledge.
Methodology of Science
Authors: Krauss Alexander 
Published: November 2024

AbstractOver our species’ history, we evolved methodological abilities of the mind (observation, problem solving and experimentation) that we use together with increasingly complex methods developed using these abilities (controlled experimentation, statistics and X-ray methods). Science has always been grounded in these evolved methodological abilities (our universal methodological toolbox) that have enabled us to develop vast bodies of knowledge by creating sophisticated methods and tools (our adaptive methodological toolbox). As we face constraints when using our evolved abilities to do science, we design tools to reduce these constraints and greatly extend our evolved mind. Such constraints are cognitive (such as limited sensory abilities, cognitive bandwidth and memory), social (such as cultural values, norms and interests), geographic (such as differences across contexts that require conducting studies in multiple contexts) and so forth. Controlled experimentation and randomisation are for example used across fields, from biomedicine to psychology, to reduce human biases in designing, implementing and analysing studies. Magnetic resonance imaging (MRI) enables us to detect phenomena like magnetic fields and radio waves that we do not have sensory receptors for. Electron microscopes vastly enhance our visual capability and enable us to perceive miniscule objects using the wavelength of an electron. Yet our tools of science can also face constraints in how we represent and model the dynamic character of phenomena using them and they have a set scope with which we can design, implement and evaluate experiments, and interpret results.
Scientometrics and Network Science
Authors: Krauss Alexander 
Published: November 2024

AbstractScience describes and explains the world through research articles and books that are organised into scientific fields. Scientometricians including network scientists analyse this scientific literature. To do so, they rely on the indicators of citations and publication counts to study issues such as research productivity, team collaborations, career dynamics, networks of scientists and institutions, and novelty in science. They use large-scale data (big data) and network analysis and search for patterns in such data. That is scientometrics in a nutshell. Scientometrics is of interest to all researchers as citations and publication counts largely determine whether researchers get research grants, academic jobs and promotions. Most current measures of scientific impact and success still use citations. Our current reward system in science is deeply embedded in this metric.
Statistics and Mathematics of Science
Authors: Krauss Alexander 
Published: November 2024

AbstractStatistics and mathematics are arguably the two most widely used methods across science and they have fundamentally transformed how we do science and how we understand the world. Statistics involves the collection, analysis and interpretation of data, whereas most scientific theories are formalised using the mathematical methods of algebra and calculus, such as the equation E = mc2. In many fields of science, inferential statistics has become synonymous with the scientific method. Modern statistics has revolutionised empirical sciences by enabling us to study the world with vast amounts of data in more complex ways, conduct and analyse larger-scale experiments and generate causal evidence. We apply statistical methods to study basically any phenomenon in science, from cells and viruses in populations, to planets, economic markets and science itself. Yet a central limitation of statistical and mathematical methods is that many aspects of the world cannot be easily studied using them since they cannot be captured well quantitatively. There are also a number of problems that commonly face statistical studies and affect the quality of our evidence. These involve small samples and small effect sizes in studies, p-hacking and statistical standards including the statistical significance level that researchers apply in their studies. Such statistical issues have contributed to a replication crisis in science.
Science of Science: An Integrated and Methods-Driven Understanding of Science
Authors: Krauss Alexander 
Published: November 2024

AbstractA unified account of the field of science of science is presented here that enables a more comprehensive understanding of the origins, foundations and limits of science—and thus a holistic picture of science. This is done by synthesising the evidence and approaches across the 14 discussed subfields of science of science—namely evidence across the natural, behavioural and social sciences. What enables and constrains science cannot be explained from a single disciplinary perspective alone. Taking such a holistic approach represents the most comprehensive understanding we have of science for the following reason: the range of disciplinary approaches apply different methods and focus on different features of science, and there is coherence across the independent strands of evidence, in particular in the role of methodological features in shaping science. Our methodological toolbox is at the centre of science and enables us to do and advance science in new ways.
Conclusion and Implications
Authors: Krauss Alexander 
Published: November 2024

AbstractViewing the field of science of science through an integrated lens can provide answers to fundamental questions about science: its origins, foundations and limits and how to push those limits. This book aims to offer a foundation for the integrated field of science of science by combining methods and evidence from across the natural, behavioural and social sciences. Science of science, conceived here as an integrated field, provides a unified understanding of science and how to improve science by identifying the abilities and conditions that drive and constrain science. Our methodological toolbox underlies the different factors across disciplinary perspectives. Four main implications are outlined here of this meta-approach to studying science, including the need to establish a society, journals, conferences and interdisciplinary institutes, better train researchers studying science and conduct research in a more interdisciplinary way, better understand the constraints we face in science to better reduce them, and adopt a broader set of empirical methods to better address foundational questions about science.
Changing perspective from being to becoming—An alternative approach to language development and speaker categorization based on longitudinal data
Authors: Stopfner Maria 
Published: November 2024

Abstract
In the face of transnational mobility and migration, globally networked communities and super-diverse social environments, traditional research practices of speaker categorization such as the distinction between native and non-native speakers, first, second, and third language users and mono-, bi-, and pluri-/multilinguals, which rest on the assumption of categorical differences between types of speakers and the general stability of speaker categories as biographical fact, are increasingly called into question. Critical voices point out that in real life, differences between language users turn out to be fuzzy, gradient, and continuous, and that conventional speaker categorizations are conceptualized from a monolingual perspective, providing grounds for discriminatory practices. Based on a longitudinal study design, the paper aims to offer an alternative quantitative approach for researchers who are dissatisfied with common practices of speaker categorization and wish to shift perspective to a more comprehensive way of studying language development within communities. The analysis draws on the results of c-tests for German, Italian, and English that were specifically designed for the study and administered to the same students (n = 170) in their first and final year at lower secondary school.
Phospholipid dynamics in Aspergillus species: relations between biological membrane composition and cellular morphology
Authors: Iwama Ryo 
Published: November 2024

Abstract
Biological membranes, primarily composed of phospholipid bilayers, are essential structures that compartmentalize the cell from the extracellular environment. The biosynthesis and regulation of membrane lipids have been extensively studied in model organisms such as Saccharomyces cerevisiae and mammalian cells. However, our understanding of biological membrane regulation in filamentous fungi, some of which are significant in medicine, pharmacy, and agriculture, remains limited. This minireview provides a comprehensive overview of the latest knowledge, focusing on filamentous fungi of Aspergillus species. Recent progress in understanding dynamic changes in membrane lipid profiles, driven by improvements in analytical techniques for lipidomics, is also presented. Furthermore, known that the cell morphology of filamentous fungi is closely linked to its harmful and beneficial characteristics, the influence of membrane composition on cell morphology is discussed. The integration of these findings will further enhance our understanding of the biological functions of membranes in filamentous fungi.
Ultrasound S-detect system can improve diagnostic performance of less experienced radiologists in differentiating breast masses: a retrospective dual-centre study
Authors: Du Liwen , Liu Hongli , Cai Mengjun , Pan Jiazhen , Zha Hailing , Nie Chenlei , Lin Minjia , Li Cuiying , Zong Min , Zhang Bo 
Published: November 2024

Abstract

Objective
To compare the performance of radiologists when assisted by an S-detect system with that of radiologists or an S-detect system alone in diagnosing breast masses on US images in a dual-centre setting.

Methods
US images were retrospectively identified 296 breast masses (150 benign, 146 malignant) by investigators at 2 medical centres. Six radiologists from the 2 centres independently analysed the US images and classified each mass into categories 2-5. The radiologists then re-reviewed the images with the use of the S-detect system. The diagnostic value of radiologists alone, S-detect alone, and radiologists + S-detect were analysed and compared.

Results
Radiologists had significantly decreased the average false negative rate (FNR) for diagnosing breast masses using S-detect system (−10.7%) (P &lt; .001) and increased the area under the receiver operating characteristic curve (AUC) from 0.743 to 0.788 (P &lt; .001). Seventy-seven out of 888 US images from 6 radiologists in this study were changed positively (from false positive to true negative or from false negative to true positive) with the S-detect, whereas 39 out of 888 US images were altered negatively.

Conclusion
Radiologists had better performance for the diagnosis of malignant breast masses on US images with an S-detect system than without.

Advances in knowledge
The study reported an improvement in sensitivity and AUC particularly for low to intermediate-level radiologists, involved cases and radiologists from 2 different centres, and compared the diagnostic value of using S-detect system for masses of different sizes.
Recurrence-free survival prediction for anal squamous cell carcinoma after chemoradiotherapy using planning CT-based radiomics model
Authors: Tang Shanshan , Wang Kai , Hein David , Lin Gloria , Sanford Nina N , Wang Jing 
Published: November 2024

Abstract

Objectives
Approximately 30% of non-metastatic anal squamous cell carcinoma (ASCC) patients will experience recurrence after chemoradiotherapy (CRT), and currently available clinical variables are poor predictors of treatment response. We aimed to develop a model leveraging information extracted from radiation pretreatment planning CT to predict recurrence-free survival (RFS) in ASCC patients after CRT.

Methods
Radiomics features were extracted from planning CT images of 96 ASCC patients. Following pre-feature selection, the optimal feature set was selected via step-forward feature selection with a multivariate Cox proportional hazard model. The RFS prediction was generated from a radiomics-clinical combined model based on an optimal feature set with 5 repeats of nested 5-fold cross validation. The risk stratification ability of the proposed model was evaluated with Kaplan-Meier analysis.

Results
Shape- and texture-based radiomics features significantly predicted RFS. Compared to a clinical-only model, radiomics-clinical combined model achieves better performance in the testing cohort with higher concordance index (0.80 vs 0.73) and AUC (0.84 vs 0.78 for 1-year RFS, 0.84 vs 0.79 for 2-year RFS, and 0.85 vs 0.81 for 3-year RFS), leading to distinctive high- and low-risk of recurrence groups (P &lt; .001).

Conclusions
A treatment planning CT based radiomics and clinical combined model had improved prognostic performance in predicting RFS for ASCC patients treated with CRT as compared to a model using clinical features only.

Advances in knowledge
The use of radiomics from planning CT is promising in assisting in personalized management in ASCC. The study outcomes support the role of planning CT-based radiomics as potential imaging biomarker.
Artificial intelligence research in radiation oncology: a practical guide for the clinician on concepts and methods
Authors: Hoebers Frank J P , Wee Leonard , Likitlersuang Jirapat , Mak Raymond H , Bitterman Danielle S , Huang Yanqi , Dekker Andre , Aerts Hugo J W L , Kann Benjamin H 
Published: November 2024

Abstract
The use of artificial intelligence (AI) holds great promise for radiation oncology, with many applications being reported in the literature, including some of which are already in clinical use. These are mainly in areas where AI provides benefits in efficiency (such as automatic segmentation and treatment planning). Prediction models that directly impact patient decision-making are far less mature in terms of their application in clinical practice. Part of the limited clinical uptake of these models may be explained by the need for broader knowledge, among practising clinicians within the medical community, about the processes of AI development. This lack of understanding could lead to low commitment to AI research, widespread scepticism, and low levels of trust. This attitude towards AI may be further negatively impacted by the perception that deep learning is a “black box” with inherently low transparency. Thus, there is an unmet need to train current and future clinicians in the development and application of AI in medicine. Improving clinicians’ AI-related knowledge and skills is necessary to enhance multidisciplinary collaboration between data scientists and physicians, that is, involving a clinician in the loop during AI development. Increased knowledge may also positively affect the acceptance and trust of AI. This paper describes the necessary steps involved in AI research and development, and thus identifies the possibilities, limitations, challenges, and opportunities, as seen from the perspective of a practising radiation oncologist. It offers the clinician with limited knowledge and experience in AI valuable tools to evaluate research papers related to an AI model application.
BS O03 - One-stage conversion of Adjustable Gastric band (AGB) to One-anastomosis Gastric Bypass (cOAGB) versus Sleeve Gastrectomy (cSG): A single-center experience with a short and mid-term Follow-up
Authors: Hassan Basma , Awad Philobater 
Published: November 2024

Abstract

Background
Laparoscopic adjustable gastric band was a common bariatric procedure . However; due to its failure rate some patients sought another revisional surgery. In which, we compared two of the most common Bariatric metabolic surgery (BMS) performed nowadays: the laparoscopic sleeve gastrectomy and laparoscopic one- anastomosis gastric bypass.

Method
A prospective cohort study was conducted from June 2020 to June 2022 at a single medical center, which included 77 patients undergoing single-stage conversion to (cOAGB) vs (cSG). Patients were reassessed for weight loss, associated medical problems remission and post-operative complications at 6, 12, and 18 months.

Results
Group (I) was 43 patients who underwent cOAGB and Group (II) was 34 patients who underwent cSG. The BMI was calculated post-operative at three visits. Both groups had no statistically significant values at 6 and 12 months follow-up. However, patients who underwent cOAGB after 18 months of follow-up achieved lower BMI than those who underwent cSG . Regarding EBWL% there was a statistically significant difference between the two groups after 6, 12 and 18 months follow-up. Regarding associated medical problems remission with follow-up 18 months post-operative , there were no statistically significant differences between the two groups

Conclusion
This study suggests that the conversion of LAGB to either cOAGB or cSG could be feasibly performed in a single-stage operation. cOAGB had a significant difference as regards the weight loss results than cSG among the mid-term follow-up. However, there is no significant difference in the postoperative complications and the resolution of the co-morbidities. Therefore, cOAGB could provide a reliable alternative but needs to be substantiated in future long-term studies.
OGC SO15 - Utility of radiological re-staging following neo-adjuvant chemotherapy and its impact on oncological outcomes in resectable gastric adenocarcinoma
Authors: Ngo Vivien , D’Souza Joel , Alam Mushfique , Stephens Nathan , Pritchard Mark 
Published: November 2024

Abstract

Background
The current perioperative chemotherapy regimen that comprises the multimodal treatment strategy for gastric cancer consists of Fluorouracil, Leucovorin, Oxaliplatin, and Docetaxel. Despite improvement in outcomes at a population level, real-world data suggests that ≤40% of patients show a poor/no response to the neoadjuvant phase of treatment. Identification of these poor/non-responders may facilitate a re-evaluation of their treatment strategy and prevent potentially futile surgery. Radiological re-staging following neoadjuvant treatment is routinely performed despite its debatable accuracy and benefits. This study aimed to define the accuracy of radiological re-staging and its impact on survival following neoadjuvant treatment for resectable GC.

Method
Patients with resectable gastric cancer who completed 4 cycles of neoadjuvant FLOT and underwent resectional surgery with curative intent between 2018 and 2022 were retrospectively identified. All patients were clinically staged with gastroscopy, CT and staging laparoscopy. The index radiological/clinical stage at diagnosis was compared to the radiological re-staging following neoadjuvant treatment. Patient were re-staged with either a CT or PET-CT. Both were then compared with the final pathological stage to assess for correlation between the radiological and pathological stage. A survival analysis was then performed to evaluate the impact of radiological-pathological staging discrepancy on overall survival

Results
A total of 65 consecutive patients met the inclusion criteria of this study. On comparison of the index clinical stage with the radiological re-stage post-neoadjuvant treatment, 71% had concordant stages, 25% were downstaged, and 4% were upstaged. On comparison of the index clinical stage with the final pathological stage, 35% had concordant stages, 38% were upstaged and 27% were down staged (Cohen kappa = -0.224, 95% CI -0.390, -0.058). Discrepancy between clinical stage and radiological re-stage did not influence survival. However, discrepancy between clinical and pathological stage highlighted survival differences where upstaged patients had reduced survival (p = 0.018).

Conclusion
The accuracy of clinical staging compared to final pathological staging in the setting of aggressive tumour biology remains undefined. Radiological re-staging following neoadjuvant treatment proves to be an unreliable tool for assessing treatment response and does not predict long-term survival outcomes. These findings highlight the importance of accurate staging and the need for more precise methods of evaluating treatment response. This may enhance decision-making, individualise treatment regimens, and improve patient outcomes.