dataesr/scientific-paragraphs-categorization huggingface dataset:
We also used two different seeds, 1604 and 1993, to assess the potential impact of random fluctuation on the results. For each scenario, the treatment effect was quantified using both the odds ratio and the risk difference, with the point estimate and the estimated variance obtained using both Rubin's rules and Reiter's rules. We computed the relative difference between Rubin's and Reiter's rules point estimates and variances defined as the difference between Rubin's and Reiter's rules point estimates and variances over the Reiter's rules point estimate and variance. This allows the comparison of the two approaches in a more standardized way than just looking at the raw differences. Figure  4  shows these relative difference for all scenario considered. For both Rubin's rules and Reiter's rules, the point estimate is obtained as the mean of estimated treatment effects across imputed datasets.
However, because inflated standard errors were observed in the absence of missing data, the authors attributed the over-coverage to the standard error estimator being conservative, rather than being a consequence of applying multiple imputation in this setting. Reiter's rules have the advantage of being easy to implement using the R package mice and its ignore argument as we detailed on Section 2. A drawback of Reiter's rules, however, is that when doing nb imp imputations for each of the nb rep parameter draws this leads to a total of nb imp × nb rep imputed datasets, requiring the process of propensity score matching and treatment effect estimation to be repeated nb imp × nb rep times. The choice of (nb imp ; nb rep ) when using Reiter's rules is therefore important and is a compromise between the computational burden and the precision of the method.
When working with big sample sizes, implementing Reiter's rules may become computationally burdensome. However, in many standard situations with modest sample sizes, this is not an issue and Reiter's rules can be easily applied. In this article, we have focused on the propensity score matching approach only because the issue of inflated variance only arises with this propensity score method. This is because among the various propensity score approaches, matching is the only one which discards a large portion of patients from the initial dataset leading to an inconsistency between the sample used to impute the missing data and the one used to estimate the treatment effect. In our simulation settings, using the IPTW approach to estimate the average treatment effect using Rubin's rules to compute the variance results in coverage rates close to the nominal value, consistent with results from previous work.
Descriptive statistics and missing data summary for potential confounders used in our illustrative example from the National Cancer Registry dataset according to the outcome: absence or presence of surgery TA B L E 1 No surgery received Surgery received Overall Surgery (N = 26 501) (N = 4850) (N = 31 351) Age (binary) < median 12 371 (46. 7%) 3304 (68. 1%) 15 675 (50. 0%) > median 14 130 (53. 3%) 1546 (31. 9%) 15 676 (50. 0%) Sex Male 14 729 (55. 6%) 2463 (50. 8%) 17 192 (54. 8%) Female 11 772 (44. 4%) 2387 (49. 2%) 14 159 (45. 2%) Stage Early 3339 (12. 6%) 3823 (78. 8%) 7162 (22. 8%) Late 20 488 (77. 3%) 878 (18. 1%) 21 366 (68. 2%) Missing 2674 (10. 1%) 149 (3. 1%) 2823 (9. 0%) Performance status Good 9332 (35. 2%) 3728 (76. 9%) 3728 (76. 9%) Bad 10 309 (38. 9%) 340 (7. 0%) 10 649 (34. 0%) Missing 6860 (25.
Study population Patients from RA-BIODAM (BIOmarkers of joint DAMage) were included  [11]. RA-BIODAM is a 2-year multi-national prospective observational study, including patients with a clinical diagnosis of RA and also fulfilling the 2010 RA Classification Criteria  [12] , recruited in daily practice from 10 countries from October 2011 to April 2015. At inclusion in the cohort, patients had active disease (44-joint disease activity score, DAS44 > 2. 4)  [13]  and were to be started on or to change DMARD treatment, including conventional synthetic DMARDs (csDMARDs) and a first TNF inhibitor (TNFi); patients who had prior biologic (b)DMARD experience were excluded. Patients were included in this analysis if they had at least one (6-, 12-or 24-month) interval with radiographic progression data available.
Radiographic damage progression Radiographic damage progression was the outcome of interest. Radiographic damage, assessed in radiographs of the hands and feet at 6-month intervals, was scored with the Sharp-van der Heijde method (SvdH) by two readers blinded for clinical data but aware of the chronological order  [14, 15]. The average score of the readers was used. SvdH measures erosions and joint space narrowing in 44 different joints and provides an aggregated sum score ranging from 0 to 448. Radiographic progression was computed as change scores, e. g. 6-month progression reflects the difference between the status scores at 6 months and baseline, the latter subtracted from the former. Progression scores were calculated for each 6-month interval, for 12-month intervals and for the whole follow-up, i.
The inclusion of individual fixed effect enables us to yield causal inference. We thank an anonymous reviewer for pointing out this issue. Regional dummies distinguish regions to which countries belong. All the countries in the sample are divided into Asian, African, European and American regions. 10 The time fixed effect is incorporated into the model to account for time-dependent macro factors that may influence both economic performance and renewable energy supply capacity, such as, financial crises, external energy demand shocks, and changes over time in global energy prices. We use a linear time trend to control for time-specific factors. 1  shows the definition of variables and data sources. After identifying the variables used in this study, we collect their data. The selection of the countries and sample period are based on the availability of annual data, spanning the period 2009 to 2019.
18 ur sample is restricted to both lower and upper middle income countries based on the World Bank's classification for the year 2019. 19 Out of the 106 countries categorized as lower/upper middle income, only 43 countries have data on the variables BIOM and WIND either for the entire period under analysis or for some years of the sample period. 20 uring the data collection process, we find that there is no data available on some variables of interest in the case of 10 countries. 21 Thus, this leaves us with a sample of 32 countries. 22 These countries are major global renewable electricity producers, collectively accounting for over half of global renewable electricity production since 2017. 23 fter collecting their data, we implement the interpolation method to fill in the missing values of variables BIOM, WIND and COAL so as to obtain a balanced panel dataset.
24 The missing values prevent us from running the spatial panel model because spatial econometrics need balanced panel data in order to take into account the spatial dependence at each point of time (Sanso-Navarro, Vera-Cabello, and Puente-Ajovín 2020). 25 In the end, our final balanced panel dataset covers 32 countries over the 11year period from 2009 to 2019, resulting in 352 observations. The descriptive statistics for all the variables across the sample after the interpolation process are shown in Table  2. 26  The correlation coefficients between disaggregated renewable energy sources and economic growth for all countries are reported in Table  3. This table suggests that BIOM/WIND and Y are highly positively and significantly correlated with each other in the majority countries. 27 hese results indicate that renewable energy sources play an important role in promoting economic activities across most countries.
31 To avoid the possible endogeneity problem, we follow Ertur and Koch (2011) by calculating the average bilateral trade over the 2009-2019 period. Data on trade flows for each country are taken from the UN Comtrade Database. 32 Row standardization allows us to interpret the spatial spillover effects as an average of all neighbours (You and Lv 2018). 33 The queen continuity criterion is one of the most popular criteria for creating geographic weight matrix in the existing literature (see e. g. Cho, Chen, and  Poudyal 2010; Leiva, Vasquez-Lavín, and Oliva 2020, among many others).
Authors' own calculation based on data from U. S. Energy Information Administration. In detail, our sample of 32 countries collectively accounts for 44. 5%, 46%, 45. 4%, 46. 8%, 46. 9%, 48%, 48. 5%, 49. 5%, 50. 1%, 51. 1%, and 52. 2% of global renewable electricity production in2009, 2010, 2011, 2012, 2013, 2014,  2015, 2016, 2017, 2018 and 2019, respectively.
We further use the novel panel Granger non-causality test, developed by Juodis, Karavias, and Sarafidis (2021) , to provide supportive evidence of one-way causality running from renewable energy sources to economic growth. This test accounts for cross-sectional dependence in the data series, efficiently estimates the panel Granger non-causality in heterogeneous or homogeneous panels and is robust for panel data with a moderate time dimension. So this test is suitable for our dataset. Under the null hypothesis, there is no causality in any cross-section units of the panel. Under the alternative hypothesis, there is causality in at least one cross-section unit. This test is applied on the first difference of the series under examination, because this test requires that all the variables must be stationary.
These finite dimensional linear programs are solved using the Gurobi 1  solver in Python (Version 9. 5. 1). In order to evaluate the convergence of the algorithm, we compute the (discrete time-space) exploitability (term borrowed from  [47] ) at each iteration: 𝜀 𝑁 = ∆ 𝑛𝑡-1 ∑︁ 𝑖=0 𝑛𝑠-1 ∑︁ 𝑗=1 𝑓 (︁ 𝑡 𝑖 , 𝑥 𝑗 , m(𝑁-1) (𝑡 𝑖 , •) )︁(︁ 𝑚 (𝑁 ) (𝑡 𝑖 , 𝑥 𝑗 ) -m(𝑁-1) (𝑡 𝑖 , 𝑥 𝑗 ) )︁ + 𝑛𝑡 ∑︁ 𝑖=0 𝑛𝑠 ∑︁ 𝑗=0 𝑔(𝑡 𝑖 , 𝑥 𝑗 , μ(𝑁-1) ) (︁ 𝜇 (𝑁 ) (𝑡 𝑖 , 𝑥 𝑗 ) -μ(𝑁-1) (𝑡 𝑖 , 𝑥 𝑗 ) )︁. In Figure  1  (left graph), we can observe the evolution of the distribution of the players 𝑚 𝑡 over time and Figure  1  (right) shows the exit distribution 𝜇 of the players. As expected, players starting at a low position exit the game immediately and players starting at higher positions exit the game at later dates. Finally, Figure  2  illustrates the convergence of the algorithm via a log-log plot (base 10) of the exploitability.
Objectives: To identify, summarize, and analyse comments on the core reporting guidelines for protocols of randomized trials (Standard Protocol Items: Recommendations for Interventional Trials [SPIRIT] 2013) and for completed trials (Consolidated Standards of Reporting Trials [CONSORT] 2010), with special emphasis on suggestions for guideline modifications. Methods: We included documents written in English and published after 2010 that explicitly commented on SPIRIT 2013 or CON-SORT 2010. We searched four bibliographic databases (Embase and MEDLINE to June 2022; Web of Science and Google Scholar to April 2022) and other sources (e. g. , the EQUATOR Network website, the BMC Blog Network, and the BMJ rapid response section). Two authors independently assessed documents for eligibility and extracted data on basic characteristics and the wording of the main comments.
Adequate reporting of a trial enables appraisal of risk of bias, the appropriate applicability of its results, and inclusion of data in a meta-analysis. Comprehensive, clear, and transparent reporting is a prerequisite for trial findings to reliably inform patient care and patient decision-making  [3]. Unfortunately, important aspects are often not reported adequately  [4]. Studies have shown that reporting of trial outcomes  [5] , blinding  [6] , sample size calculations  [7] , and allocation concealment  [8]  is often incomplete in trial protocols and completed reports. To facilitate full reporting of information in trial protocols, the Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT) guideline was developed in 2013  [1]. Similarly, to facilitate full reporting of completed trial reports, the Consolidated Standards of Reporting Trials (CONSORT) was developed in 1996 with subsequent updates in 2001 and 2010.
The purpose of updating CON-SORT was to prevent misinterpretation of specific items and to take into account new methodological developments  [3]. SPIRIT 2013 and CONSORT 2010 are endorsed by many journals, regulators, research funders, editorial groups (e. g. , the International Committee of Medical Journal Editors), and patient groups  [9, 10]  and are used routinely by trial authors. Several years have passed since the release of the guidelines, and the context of clinical trial methodology is continually evolving. Similarly, open science practices related to clinical trials (e. g. , data management plans) have started to be mandated and/or recommended. Therefore, the executive group for SPIRIT and CONSORT is planning to jointly update both reporting guidelines  [11]. Several researchers have commented on the usability of SPIRIT 2013 and CONSORT 2010.
Information sources and search for documents In a previous study investigating comments on the Cochrane tool for assessing risk of bias in randomized trials, it proved challenging to strike an appropriate balance between search sensitivity and specificity  [18]. Therefore, we used multiple approaches to identify documents. The search strategy was developed in liaison with a search specialist (L. Ø. ). First, we performed a systematic basic search in two databases: Embase and MEDLINE (from January 2010 to June 2022). We used the search strategies in Appendix 1. We reviewed the search strategy using the PRESS 2015 Evidence-Based Checklist  [19]  in collaboration with a search specialist. Second, we performed a focused search among documents citing SPIRIT 2013 or CONSORT 2010. We used Web of Science (from January 2010 to April 2022) to identify studies citing any of the SPIRIT 2013 or CONSORT 2010 statement or explanation publications listed on the SPIRIT or CONSORT websites where a Digital Object Identifier was available.
We used the refine search function and the search terms in Appendix 2 to identify potentially eligible documents. Third, we used Google Scholar (from January 2010 to April 2022) to conduct full text searches. We used standard phrases from the statement and explanation publications of the reporting guidelines and comments identified through the database searches (Appendix 3). For each search, we sorted the search records by relevance and stopped screening when no additional documents had been identified for a substantial amount of the sorted documents (O 50 records). Fourth, we searched key websites (e. g. , the EQUATOR Network website  [20] ), blogs (e. g. , the BMC Blog Network  [21] ), and responses or comments to the SPIRIT 2013 or CONSORT 2010 statement or elaboration papers (e. g. , through the BMJ rapid response section  [22] ) for additional documents (from January 2010 to April 2022).
Mechanisms and perspectives Many of the 114 comments suggested adding new content to SPIRIT 2013 or CONSORT 2010 and none suggested to delete items. SPIRIT 2013 and CONSORT 2010 aim to address the minimum content relevant for all randomized trial protocols and reports. The forthcoming revision of the guidelines needs to prioritize which new items are the most important and which changes can be implemented without introducing unnecessary complexity. New methodological developments have emerged or received increasing attention since the development of SPIRIT 2013 and CONSORT 2010. Three themes, trial authors' financial conflicts of interest, data sharing, and patient and public involvement, were highlighted in the included comments. Additional themes such as remote clinical trials  [37] , patient-centric trials  [38] , basket trials, umbrella trials, and platform trials  [39, 40]  may also be worth considering, although such issues may be better addressed in extensions than in the main reporting guidelines.
Global distribution of dementia clinical trials To determine the global distribution of dementia clinical trials, the primary data sources included ClinicalTrials. gov 17 and the World Health Organization's (WHO) established International Clinical Trials Registry Platform (WHO-ICTRP). 18 0] [21]  The search strat- egy in both trial registries used the terms: "Alzheimer," "frontotemporal dementia," "Lewy body disease," "vascular dementia," and "dementia. " F I G U R We included all trials of DMTs in Phases 1, 2, and 3. We excluded all non-pharmacologic therapeutic approaches such as devices, cognitive therapies, caregiver interventions, supplements, and biomarker validation trials. Because one clinical trial may be registered in multiple countries, we obtained data on individual trials per country/region to better represent the volume of clinical research activity in different countries.
Due to the potential of under-reporting of ongoing CT in LMICs, each regional co-author collected information on ongoing regional trials not reported in ClinicalTrials. gov or in WHO-ICTRP. This search yielded 14,202 studies as of January 2022 (Figure  1 , Panel A). Duplicate trials (n = 4992, trials included in both registries) were accurately identified and removed for a total of 3467 clinical trial studies (1237 unique DMTs). The final data set included trial title and number, beginning date, duration, planned enrollment number, primary funding source, study phase, and country. The funding source was categorized as government-sponsored (e. g. , "National Institutes of Health" or "Other Government Agency"), industry-sponsored ("Pharmaceutical companies"), Research/Academic centers ("Health care centers/University"), or a combination of the previous categories.
The clinical study phase was based on the U. S. Food and Drug Administration's (FDA's) classification system (Phase 1 was defined as "conducted with healthy volunteers and emphasize safety and dose"; Phase 2 was defined as "preliminary data on efficacy and side effects"; Phase 3 was defined as "information about safety and effectiveness by studying different populations and different dosages"). Some trials are presented as 1/2 or 2/3 in the database, and we adopted that nomenclature in the review.
Identifying barriers for conducting dementia clinical trials in LMICs To identify existing barriers and provide recommendations for conducting ADRD clinical trials in LMICs, we conducted a systematic review search and expert consensus. Details about the systematic review search are provided in Supplemental Material 2. In summary, the published literature was searched using strategies designed by a medical librarian (A. H. ) for the concepts of research or clinical trials AND barriers, with related synonyms. These strategies were created using a combination of controlled vocabulary terms and keywords and were executed in Embase. com, Ovid-Medline All, EBSCO Global Health, Scopus, and ProQuest Dissertations and Theses Global from database inception. No language or date limits were used. All database searches were completed on March 2, 2022.
A total of 1207 results were retrieved from the database literature search and exported to Endnote. Duplicate citations (n = 397) were accurately identified and removed using a technique described by Bramer et al. 22  After removing all duplicates, 810 unique citations remained for analysis and were screened for appropriateness against the inclusion and exclusion criteria. Inclusion criteria required that studies (1) reported on barriers/challenges for conducting research, specifically clinical trials within developing countries; and (2) were empirical, peer-reviewed research studies (commentaries, editorials, and literature reviews were excluded). After the abstract screening phase (led by co-authors S. D. B. , M. P. C. , K. D. , Y. C. ), studies that met the inclusion criteria (n = 37) underwent full-text assessment for eligibility (second screening stage) and were selected based on their relevance.
Eighteen (18) peer-reviewed publications were selected for the final analysis (Figure  1 , Panel B). The quality of each selected study was assessed according to a six-point scale (design, sample size, data collection, analysis, and report). Due to the limited number of reports, the purpose of the quality assessment was to determine the overall contribution and relevance of each report for the review and not to establish thresholds for study exclusion. A group composed of three of the authors (M. P. , M. I. B. , and J. J. L. G. ) summarized the literature that was found per region. Collaborative regional studies were reviewed during consensus meetings. From each research study, information on the region/country, economic development, and barriers/limitations for trial implementation was extracted.
Statistical analyses Data analysis included the number of clinical trials per country, regional distribution, funding source, study phase, and the number of clinical trial growth rates by region. We classified each country's economic development status according to the 2021 World Bank development report (https://wdr2021. worldbank. org/the-report). The number of clinical trials per country and region is presented in absolute values and relative to dementia prevalence. Dementia estimates for each country were obtained from the 2019 Global Burden of Disease Study. 27 All calculations were performed using R (v. 1. 2. 5033) statistical computing program (https://www. R-project. org/).
LMICs do not regularly report ongoing clinical trials in clinicaltrials. gov and/or WHO-ICTRP, this would be an important limitation for clinical trial implementation in LMICs. Second, our study addressed only ADRD clinical trials using DMTs. Limitations and barriers for the implementation of DMT clinical trials are likely different from the ones faced by non-pharmaceutical interventions. Future studies should explore the use of non-pharmaceutical interventions in low-resource settings and provide tailored recommendations for implementation. Third, due to the limited number of references highlighting limitations to ADRD clinical trials implementation in LMICs, our study was not designed to exclude reports based on quality assessments, which might bias some of the interpretations. It is worth noting that our study explored only regional differences by economic development status without considering country-specific disparities that might be present in urban versus rural areas.
ACKNOWLEDGMENTS The working group to increase sustained diversity in clinical trials is an initiative supported by the  Alzheimer's Association, Diversity and Disparity Professional Interest Area , the  Global Brain Health Institute , and the  Center for the Study of Race, Ethnicity & Equity. The content is solely the responsibility of the authors and does not represent the official views of the supporting institutions. Dr. Llibre-Guerra 's research is supported by the  National Institutes of Health-National Institute on Aging (NIH-NIA)  ( K01AG073526 ), the  Alzheimer's Association  ( AARFD-21-851415 ,  SG-20-690363 ), the  Michael J. Fox Foundation  ( MJFF-020770 ), the  Foundation for Barnes-Jewish Hospital , and the  McDonnell Academy. Dr. Ganesh Babulal  has had investigator-initiated research funding from the  NIH  ( R01AG056466 ,  R01AG068183 ,  R01AG067428 ,  R01AG074302 ) and  BrightFocus Foundation  ( A2021142S ).