dataesr/scientific-paragraphs-categorization huggingface dataset:
Study design and treatment The study design for CheckMate 9LA (NCT03215706) has been described previously  [9, 17]. Briefly, adult patients with treatment-naive, histologically confirmed stage IV or recurrent NSCLC, an Eastern Cooperative Oncology Group performance status of 0e1 and no known sensitising epidermal growth factor receptor mutations or anaplastic lymphoma kinase rearrangements were randomised 1:1 to receive nivolumab (360 mg intravenously every 3 weeks [Q3W]) plus ipilimumab (1 mg/kg intravenously every 6 weeks) combined with histology-based platinum-doublet chemotherapy (intravenously Q3W for 2 cycles) or chemotherapy alone (intravenously Q3W for 4 cycles). Stratification factors were sex (male/female), histology (squamous/nonsquamous) and tumour PD-L1 expression (<1% or !1%).
Characteristic, n (%) All randomised population  [17]  PRO analysis population c Patients with tumour tissue that could not be assessed for PD-L1 (maximum 10% of total randomly assigned population) were stratified with the subgroup of patients with PD-L1 <1%. NIVO þ IPI þ Chemo n Z 361 Chemo n Z 358 NIVO þ IPI þ d Data are indicated as n (%) of PD-L1equantifiable patients. effect model repeated measure (MMRM) analyses to compare treatment groups after controlling for baseline score and relevant stratification factors as covariates; data from only treatment-phase time points with !10 patients per treatment arm were included in the model. Time-to-event analyses were not limited to PRO data collected during treatment-phase time points but also included data from all follow-up time points in the all randomised population.
Time to definitive deterioration (time from randomisation until first deterioration in PRO score from baseline when the changes in PRO scores at all subsequent assessments also reach or exceed the MID) was selected as an outcome measure in the primary analysis to minimise potential bias from transient deteriorations in PROs caused by temporary treatment side-effects and to align with prior publications  [25, 26]. Times to first/confirmed deterioration or improvement were also assessed (see Supplementary Methods). Time-to-event values were estimated using KaplaneMeier methodology; a stratified Cox proportional hazards model was used to estimate HRs and associated 95% CIs. All analyses were conducted according to a prespecified analysis plan, using SAS software (version 9. 4, SAS Institute, Cary, NC).
Patients In CheckMate 9LA, 719 patients were randomised to nivolumab plus ipilimumab with chemotherapy (n Z 361) or chemotherapy (n Z 358) (Supplementary Fig. 1 ). At database lock, the minimum follow-up for OS was 24. 4 months and median (range) duration of therapy was 6. 1 (0e24. 4) months (experimental arm) and 2. 5 (0e34. 5) months (chemotherapy arm)  [17]. In the PRO analysis population, 333 (99%) and 313 (97%) patients assigned to nivolumab plus ipilimumab with chemotherapy and chemotherapy, respectively, had evaluable PROs for LCSS; 332 (99%) and 323 (100%) patients had evaluable EQ-5D data. Baseline characteristics of patients in the PRO analysis population were balanced between treatment arms and consistent with those for all randomised patients (Table  1 ). PRO questionnaire completion rates were >90% at baseline and >80% at all treatment-phase time points in which !10 patients per treatment arm had analysable PRO assessments (Supplementary Table  1 ).
Fig. 4. Longitudinal MMRM analysis: differences in LS mean (95% CI) change from baseline for LCSS ASBI and individual symptoms (A), and LCSS 3-IGI and individual items (B) at on-treatment assessments in the PRO analysis population. Data up to 102 weeks from time points with !10 patients in both treatment arms were included in the analysis. Bold values indicate difference between treatment arms. The minimally important difference was defined as 10 points for the LCSS individual items and ASBI and 30 points for LCSS 3-IGI. The differences between treatment arms were not statistically significant (p >0. 05). 3-IGI, 3-item global index; ASBI, Average Symptom Burden Index; Chemo, chemotherapy; CI, confidence interval; HRQoL, health-related quality of life; IPI, ipilimumab; LCSS, Lung Cancer Symptom Scale; LS, least squares; MMRM, mixed-effect model repeated measure; NIVO, nivolumab; PRO, patient-reported outcome.
However, conclusions are limited as these data were not statistically tested; furthermore, improvements in HRQoL from baseline in Fig. 5. Longitudinal MMRM analysis: differences in LS mean (95% CI) change from baseline for EQ-5D-3L VAS (A), and EQ-5D-3L UI (B) at on-treatment assessments in the PRO analysis population. Data up to 102 weeks from time points with !10 patients in both treatment arms were included in the analysis. Bold values indicate difference between treatment arms. The minimally important difference was defined as 7 and 0. 08 points for EQ-5D-3L VAS and UI, respectively. The differences between treatment arms were not statistically significant (p >0. 05). Chemo, chemotherapy; CI, confidence interval; EQ-5D-3L, 3-level version of the EQ-5D; LS, least squares; MMRM, mixed-effect model repeated measure; NIVO, nivolumab; PRO, patient-reported outcome; UI, utility index; VAS, visual analogue scale.
Acknowledgements This study was supported by  Bristol Myers Squibb. The authors thank the patients and their families who made this study possible, the investigators and clinical study teams who participated in the trial, and  Elizabeth Roy  of  Bristol Myers Squibb  for her contribution as protocol manager for this trial. Dako (an  Agilent Technologies company, Santa Clara, CA, USA ) participated in the collaborative development of the PD-L1 IHC 28-8 pharmDx assay. Professional medical writing support and editorial assistance were provided by  Vidya Rajagopalan, PhD , of  Evidence Scientific Solutions Inc. , funded by  Bristol Myers Squibb.
The context of Chinese investments Using TEU movement datasets and port trade flow dynamics, the China-Mediterranean trade can be framed from a maritime infrastructure connectivity perspective. According to the new trade routes and logistic-port structures related to Chinese investments, Mediterranean ports are at the frontline in the trade and investment dynamics  (Amodio 2019). The BRI has regional repercussions on the port system, determining a differentiation characterized by the attraction of economic flows and port development prospects. For instance, in 2017, the Mediterranean area handled 98. 9 million TEUs, compared to 83. 7 million in 2008, an increase of 18. 1%. This growth is not uniform, with differentiated growth rates being observed. For instance, one of the leading port and transshipment hubs, Tanger Med, went from 4% of the total Mediterranean maritime trade in 2008 to 10% in 2017.
Concentration trends of ports in the Mediterranean The Gini coefficient allows correlating the concentration of container port activity before and after the BRI in the Mediterranean, where 0 corresponds to a uniform distribution of container traffic among Mediterranean ports, while values closer to 1, underling a greater concentration. Based on port container traffic across 40 container ports, a temporal analysis for the Gini index evolution was performed for 2000, 2010, and 2019. These intervals allow for assessing if the Chinese economic insertion in the region has led to a variation in the distribution of container flows and highlight the changes in the comparative advantages within the European port system (Table  1 ). The Gini coefficient, after analysis of the databases of ISL and AlphaLiner, reveals that the concentration of the Mediterranean port system has remained similar since 2000, hovering around 0.
However, the share they have in the EU hinterland service remains marginal from these ports, there is low market share of the Hamburg-Le Havre range, in particular Antwerp, Rotterdam and Hamburg. They have a central location in relation to their hinterland. Using the Liner Shipping Connectivity Index, countries with ports having connections or direct investments with China were retained (UNCTAD 2019). The database periodically collects information on the characteristics of regular services linking Mediterranean ports and ports in China. It includes information on the regular services offered, routes and ports connected, their frequency, type of traffic transported, operators and vessels involved, and their characteristics. The variables are included in the index through an arithmetic average, from which a value of 100 is generated for the port with the highest average index in the first half of 2004.
In Finland, a 2017 survey of professional and managerial staff (Akava, 2017) found that 37% of these high-skilled Finnish workers had a NCC in their contract. Interestingly, the use of NCCs tripled for this group in the last twenty years, going from 14% of contracts signed before 2000 to 45% of contracts signed by 2015. There is some evidence for Denmark as well (Dahl and Stamhus, 2013): Business Danmark, the trade union in sales and marketing, conducted a survey in 2012 and found that about 20% of its members are subject to a NCC. Another survey run in 2012 among HK/Privat members (a union for administrative staff and the retail sector) found an incidence of NCC of 11%, mostly involving nonmanagerial positions. According to data collected by the Engineers' Association, IDA, in 2012, 14% of private sector members had a NCC.
g. , a minimum compensation is not defined. 24  This indicates that collective agreements do not play any role in the regulation of NCCs. Neither the applicable law nor collective agreements define the criteria to be used for assessing the compensation, sectoral and geographical scope of NCCs. Consequently, these issues are typically dealt with in courts. To date, conducting a quantitative analysis of case law in Italy is hardly feasible given the absence of a comprehensive database, especially regarding local and lower courts decisions. Therefore, our analysis of case law is a qualitative one, and relies on the available and most quoted decisions delivered by the Court of Cassation (the highest court in Italy). Nevertheless, to give a 21 NCCs refer to post-employment competition. Instead, in Italy competition during the employment relationship is subject to a duty of loyalty, which restricts any form of competition throughout the employment relationship.
The line between what an employee can and cannot do during the employment relationship is not always clearly defined, especially when the employee is planning to start her own business after the termination of the employment relationship (Graves, 2020). 22 985 in July 2021, when we conducted our analysis. In November 2022, 946 collective agreements were recorded in the national repository run by the National Council for Economics and Labor (CNEL). Data accessible at: https://www. cnel. it/Archivio-Contratti 23 Id. 24  On the other hand, by recalling the main legal principles and rules, these three collective agreements might help workers being more aware of these legal provisions. "quantitative" sense of the perception of this phenomenon as it looks like if we consider only the case law, we search for trials involving a NCC on Dejure 25  , one of the most comprehensive legal databases in Italy.
Survey implementation To collect responses to the survey, we hired a survey and data collection firm, which runs a panel of 150,000 active members, representative of the entire Italian population. 31  In a first phase, from this panel we randomly sampled respondents to reach a target of 2,000 completed interviews by private sector employees aged 16+, therefore excluding from the sample all public sector employees, the self-employed, the unemployed and inactive (students, pensioners, etc. ). Each prospective respondent was sent the survey via e-mail and only invited panellists 32  could respond to the survey as the link was individualised and could not be re-used. Quotas, i. e. constraints on the numbers of respondents with particular characteristics or sets of characteristics, were used to control the characteristics of the final sample to align it with the distribution in terms of age, gender and geographical area of private sector employees in Italy using the information available in the Registro Statistico Asia-occupazione by Istat (see Table  8  and Table 9 in Annex 2 ).
Data Sample The COVID-19 period under scrutiny ranges from 1 November 2019 to 31 October 2020. Daily data were extracted from Datastream and FRED. Descriptive statistics are given in Appendix A. Regarding the selection of assets, as exchange rates and gold are essential safe-haven assets, we select them because they have specific characteristics distinct from other financial assets, such as their returns being much less volatile than those of cryptocurrencies and traditional stock markets. Why should the results be different for gold-based tokens? Gold-backed cryptocurrencies represent an exciting mixture between gold (an ancient store of value) and cryptocurrency that potentially represent new-age money. While many investors like cryptocurrencies backed by gold for their more tangible characters, gold cryptocurrencies have counterparty risks and are more centralized than actual cryptocurrencies.
Safe-Haven Results From the review of the previous literature in the introduction, we can hypothesize that gold is generally considered a safe haven in the short and long term. By contrast, Bitcoin seems to exhibit no safe-haven characteristics against the S&P 500, or is only effective as a short-term safe haven, whose role is more about long-term diversification. Thus, we can assume that gold is more efficient than Bitcoin in times of crisis, such as COVID-19. Therefore, it is not without interest to favor cryptocurrencies backed by gold over those not backed by this asset, which is considered a refuge for value. In Figure  19 , we provide the plots of the stationary log-returns on which the  [1] 's regressions are run. All the calculations have been performed using the R package rmgarch  [29].
Robustness Checks The announced performance (between 7. 2% and 8. 4%) could be applicable only during phase I when the COVID-19 pandemic broke the news, and caused lockdowns worldwide. Figure  25  displays the U. S. recorded cases of COVID-19 according to John's Hopkins university database. The few cases recorded in March 2020 can be misleading, since, at the beginning of the pandemic, there were no available self-test kits for the COVID-19 virus, little availability of face masks, and long lines in pharmacies to get tested. Nevertheless, Figure  25  is useful for detecting COVID-19 waves: 1. From 2019-11-01 to 2020-10-31; 2. From 2020-11-01 to 2021-05-31; 3. From 2021-07-31 to 2022-03-31. Approximately, we could therefore identify phase I before/after the lockdown period of March 2020, then phase II from November 2020 to May 2021 with another climb of epidemic cases (and public campaigns of renewed vaccines for the whole population), and finally, phase III from August 2021 (with a considerable peak visible in U.
Acknowledgments: For comments and feedbacks, I wish to thank  Aidas Malakauskas ,  Sofiane Aboura ,  Jose Manuel Carbo Martinez ,  Michael Froemmel  and  Efe Cotelioglu  as well as conference participants at the 11th International Research Meeting in Business and Management (IRMBAM 2022 , Nice, France ), the 16th International Conference on Computational and Financial Econometrics (CFE 2022 , London, UK ), and the  8th Paris Financial Management Conference (PFMC 2022, Paris, France).
Data Availability Statement: Data were last downloaded in December 2022 from private and public repositories such as the Refinitiv Eikon Datastream https://www. refinitiv. com/en/products/ datastream-macroeconomic-analysis accessed on 19 January 2023 and FRED https://fred. stlouisfed. org/ accessed on 19 January 2023. Tickers used from FRED and the Datastream can be transmitted per request to the author. The proprietary (licensed) data cannot be transmitted.
Acknowledgments The authors wish to thank several people for their support in developing our manuscript. First, they thank  Coline Serres  and  Sylvia Dorado  for their careful revision of their first draft during the SEE conference, and similarly to all participants and organizers of the 2022 Seminar in Stakeholder Theory at the  Darden School of Business. Next, they thank  Guido Palazzo ,  Luc Sels ,  Stefanie Friedel , and  Jozef Cossey  for their.
Funding The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was funded by  FWO-SB   Fellowship Adrien Billiet , grant/award number:  1S95420N ;  FWO-SBO  project, grant/award number:  S006019N ; and  KU Leuven C2- project, grant/award number:  C24/18/004.
Geometric Model for Intermittency The objective of our work was to find a multiscale geometric model for the intermittency phenomenon. For this purpose, we proposed a simple model, comprising a clustering of points taking the form of a two-dimensional Cantor with a symmetry scale θ that represented the intensity of the clustering intermittency. The model started by distributing points randomly on a plane in a homogeneous way using MATLAB software. Then, we introduced a differentiation for the density by defining zones where the density of the points would be higher. In these areas, the points would be distributed randomly, but with a higher density than in the first step, following the 2D Cantor geometry. The differentiation process was then reproduced across scales. We, thus, associated the homogeneous distribution of points with a fractal differentiation mechanism that reproduced many features of the intermittent phenomenon.
Then, at the statistical analysis level, we applied the extended self-similarity hypothesis (E. S. S. ) to the model; a deviation from the homogeneity assumed by Kolmogorov in the fully developed turbulence was noticed. This highlighted the intermittency dynamics as a multiscale structure linking the levels of fluctuations. One of the potential applications of our work is related to wall turbulence, which needs better understanding of the involved phenomena. Our approach is related to experimental near-wall turbulence. The study of the phenomenon of intermittency in wall turbulence based on ESG and the notion of scale entropy used to validate the clustering fractal model was applied to an experimental database of boundary layer flows  [3]. This geometrical approach was validated with experimental data  [57]  considering a set of 50 cases corresponding to 50 values of y+ ranging from 1 to 2722.
Hence, using the qualitative in-depth interpretative explanation was more appropriate in this study. We sought answers to what challenges businesses faced during the pandemic and how and why individuals made the decisions they did to manage them  (Crick, 2021). Data for the study was collected from multiple sources. Information regarding the Indian film industry's economic contributions and employment data before the pandemic and the impact during the pandemic was collected from industry reports and newspaper and magazine articles. These reports and websites also allowed us to capture any formal institutional policies and decisions that influenced the activities of the B2B firms in the Indian film industry. Data regarding the actions of the stakeholders in the industry and the network relations between businesses were captured using multiple in-depth semi-structured interviews with various key informants.
The documentation and text analysis provided the background and helped us identify the key stakeholders in the sector. This step was crucial in understanding the role of the formal and informal institutions and the industry structure, which we use to provide contextual explanations for our findings. Thus, the primary and secondary data provided a comprehensive understanding of the phenomenon under study  (Ghauri & Gronhaug, 2005). The interviewees were selected using purposive sampling to enhance the research findings' goodness of fit  (Robinson, 2014). They represented multiple stakeholders, including the director of a film festival, the President of a Film Chamber of Commerce, the chief operating officer of a studio/movie house, partners of movie houses, and distributors. Selecting key informants who represent different business organizations allowed us to analyze the phenomenon under study from various perspectives  (Stake, 2013)  and verify the interview responses (Ivanova-Gongne, Koporcic, Dziubaniuk, & Mandják, 2018).
Empirical fieldwork in two healthcare organizations (HCOs) To conduct empirical fieldwork, data were collected according to qualitative ethnographic study principles  [33, 36]  at two HCOs specializing in oncology. HCOs were selected using several criteria  [33]  to observe and characterize different NC roles (Table  1 ). Observed NCs belonged to in-/out-patient wards or centralized structures (these structures are coordination platforms outside in-patient wards, but part of HCO work organizations). Data were collected by a researcher (MXA) at HCO1 and HCO2 for 1 and 3 months, respectively. Observations were conducted by shadowing  [36]  and timing activities based on time-motion studies principles  [37]. We observed one NC at HCO1 and five at HCO2 (20 and 129. 5 observation hours at HCO1 and HCO2, respectively).
Our research was based on human and social sciences methods and does not require institutional review board authorization in France  [38, 39]. Finally, secondary data sources were collected (reports, prescriptions, archival records, and job descriptions). For data analysis, we used thematic analyses. Observation data were analyzed based on three concepts of a preliminary framework. First, the study authors individually conducted data coding exercises in Excel spreadsheets (one Excel sheet/concept) using preliminary framework items as a grid. Afterwards, points of contention were discussed at meetings. This approach: (1) confirmed initial framework concepts where empirical data helped develop concepts as analysis categories by enriching definitions, or (2) created new categories where empirical data were used to define these categories.
Our objective was to review all theoretical care coordination frameworks and distinguish between macro-(health system), meso-(institutional), and micro-levels (coordination activity). We comprehensively sought all references on the subject, irrespective if activities were performed by a PPC role or within oncology. In April 2019, for data collection, we queried four databases (PubMed, Scopus, Web of Science, and ScienceDirect) using Preferred Reporting Items for Systematic Reviews and Meta-Analyses principles  [40]. Searches were performed using the following keyword combinations: (1) ("Care Coordination" OR "Coordination of Care") AND (2) (Management OR Organization), AND (3) (Theoretical Framework OR Theoretical Analysis). We limited our search to articles in English, French, and Spanish (the latter languages required an English abstract for keyword research), without time limits.
Study limitations Our study had several limitations. Firstly, empirical data were gathered from only two HCOs. To generate a more comprehensive view of coordination activities in patient pathways, a more in-depth analysis of several institutions is warranted, e. g. , ambulatory care. Also, the study was conducted within an oncology context, therefore, we encourage more studies in other clinical contexts. In both instances, confrontation of empirical data with a literature review provided a general scope for the proposed framework. Secondly, the literature review was applied to the coordination concept without reference to PPC roles. Consequently, specific NC activities may have been overlooked, however, our empirical study data, which specifically focused on NC activity, reduced possible bias.