dataesr/scientific-paragraphs-categorization huggingface dataset:
987; N = 219; Y = 1974)  ( Bennett and Snyder, 1976 ) , and #12 (S = 0. 993; N = 124; Y = 1986)  ( Titeler et al. , 1988 ). This predominance of research on LSD ends in the 1990s with a decade of research on 3,4methyl enedioxy methamphetamine (MDMA) reflected throughout 'MDMA side effects', cluster #14 (S = 0. 993; N = 116; Y = 1990)  ( Henry et al. , 1992 ) , and cluster #1 (S = 0. 969; N = 325; Y = 2001)  ( Green et al. , 2003 ). The literature then started to focus on efficacy, initially psychedelics assisted-psychotherapy for patients presenting PTSD, 'MDMA assisted psychotherapy' #16 (S = 0. 982; N = 95; Y = 2010)  ( Mithoefer et al. , 2013 ) , which was effective in four phase 2 trials (NCT00090064, NCT01211405, NCT01793610, NCT00353938) and one phase 3 trial (NCT03537014) for PTSD  ( Feduccia et al.
Analysis of co-cited journals We identified 2,560 different journals in our dataset, with the highest number of relevant papers being published in Psychopharmacology (n = 483), Forensic Science International (n = 355), and Journal of Psychopharmacology (n = 324) (Supplementary Fig. 9  and Supplementary Table  5 ). We extracted the network of co-authors journal network  that uses as unit the authors' cocited journals. This network reveals the macro-structure of scholarly disciplines through analysis of journal titles (Supplementary Fig. 10 ). The modularity score was significant, and the silhouette suggested highly credible clusters (Q = 5265; S = 0. 831). Seven cluster were identified: the first cluster, #0 'anesthesiology journals' encompassed the greatest number of co-citations, followed par #1 'psychiatry/neurology/neuroscience journals', #2'chemistry journals', #3'pharmacology journals', #4'toxicology journals', #5'forensic/chemistry journals', and #6'addictology journals'.
Specifically, in depression several phase 2 trials have been completed, with very promising results; both ayahuasca  ( Palhano-Fontes et al. , 2019 )  and psilocybin (NCT03181529)  ( Davis et al. , 2021 )  outperformed placebo, and psilocybin was also non-inferior to escitalopram (NCT03429075)  ( Carhart-Harris et al. , 2021 ). For PTSD, MDMA-assisted psychotherapy outperformed control condition in as many as four phase 2 trials (NCT00090064, NCT01211405, NCT01793610, NCT00353938) and one phase 3 trial (NCT03537014)  ( Feduccia et al. , 2019 ; Jerome et al. , 2020 ; Mitchell et al. , 2021 ; Mithoefer et al. , 2019 Mithoefer et al. , , 2018 Mithoefer et al. , , 2013 Mithoefer et al. , , 2011 ; ; Oehen et al. , 2012 ). Beyond these positive results in depression and PTSD, additional clinical test-ing has stemmed from the successful results described above, with new randomized controlled trials investigating psychedelics in anorexia nervosa, and pain  ( Bouida et al.
, 2020 ; Singh et al. , 2020 ) , in addition to the frequent use of esketamine, both intravenously and intranasally, for resistant depression  ( Fava et al. , 2020 ; McIntyre et al. , 2020 ) , additional research has focused on targeting bipolar depression. Recently, ketamine outperformed placebo in a phase 2 trial targeting suicidal ideation (NCT01944293). Beyond mood disorders, ketamine has also been tested in alcohol use disorder with promising (day of abstinence), yet not convincing (no difference on relapse rates) results in a phase 2 trial (NCT02649231)  ( Grabski et al. , 2022 ) , as well as in cocaine use disorder with improvement of motivation to quit and craving in a phase 2 trial (NCT01790490)  ( Dakwar et al. , 2014 ). When taking a closer look at the very most recent trends of the translational shift of psychedelics and dissociative substances from pharmacology to clinical testing, some further transformative trends emerged, again consistent with the two identified research trends.
Limitations This work has several limitations, partly due to the nature of scientometric analysis, which rely mostly on cocitations, and do not necessarily indicate the quality of research  ( Greenberg, 2009 ). Hence, highly cited or central cocited references are not necessarily those of highest quality. Another limitation, is the use of only one database, mainly due to the necessity of extraction both full references and citations lists, and the absence of reliable automatic duplicate removal for very large dataset with incomplete Digital Object Identifiers (DOIs)  ( Visser et al. , 2021 ). Furthermore, produced networks -in particular co-authorship networksmust be interpreted with caution as it is entirely dependent of the extracted dataset, which did not undergo systematic screening. Finally, this scientometric approach does not assess the content of included publications, and so requires clinical and methodological knowledge to interpret findings.
Declaration of Competing Interest Gabriella Gobbi , received a grant from  Diamond Therapeutics Toronto  and she is the inventor of a patent on the use of  LSD. Joshua Rosenblat  has received research grant support from the  Canadian Institute of Health Research (CIHR), Physician Services Inc (PSI) Foundation ,  Labatt Brain Health Network, Brain and Cognition Discovery Foundation (BCDF) ,  Canadian Cancer Society ,  Canadian Psychiatric Association ,  Academic Scholars Award ,  American Psychiatric Association, American Society of Psychopharmacology, University of Toronto, University Health Network Centre for Mental Health ,  Joseph M. West   Family Memorial Fund and Timeposters Fellowship  and industry funding for speaker/consultation/research fees from  Janssen ,  Allergan ,  Lundbeck ,  Sunovion  and  COMPASS.
eu, a publicly available payments database. We conducted content analysis and descriptive statistical analysis. Results: In 32 of 37 studied countries, all R&D payments were reported without named recipients, following a selfregulatory approach developed by the industry. The methodological descriptions from 125 companies operating in the United Kingdom suggest that within the self-regulatory approach companies had much leeway in deciding what activities and payments were considered as R&D. In five countries, legislation mandated the disclosure of R&D payment recipients, but only in two were payments practically identifiable and analysable. In 17 countries with available data, R&D constituted 19%-82% of all payments reported, with self-regulation associated with higher shares. Available companylevel data from three countries with self-regulation suggests that R&D payments were concentrated by big funders, and some companies reported all, or nearly all, payments as R&D.
12, 23 The outsourcing typically entails working with contract research organisations (CROs) which manage links with research partners. 12, 23, 28 3] [34] [35]  One key area of concern is conflicts of interest (COIs) that may arise in the industry's research collaborations with hospitals, universities, as well as individual scientists and clinicians. 7] [38] [39] [40]  For example, financialization reinforces concerns about COIs caused by the blurring of divisions between academia and corporations. 41 Similar criticisms have been made regarding COIs associated with collaborations managed by some CROs. 28, 42 4] [45] [46] [47]  For example, the US Sunshine Act currently mandates pharmaceutical and medical device companies to publish, in the Open Payments database, payments exceeding $10 made to physicians and teaching hospitals.
Implications for the public Creating new therapies often involves drug companies paying healthcare professionals and institutions for research and development (R&D) activities, including clinical trials. Although R&D payments are worth billions of euros, it remains unknown who receives them in most European countries. Consequently, we cannot tell whether such payments create conflicts of interest (COI) that may affect the actions of those who receive them in ways potentially compromising good scientific practice or patient care. We explain how payments for R&D activities could be made more transparent by creating an easily accessible, searchable online database that anyone could use to check the details of companies making R&D payments, their recipients, payment value and purpose. On its own, increased transparency of R&D payments is unlikely to prevent bias and unethical behaviour, where such exist.
77, 81 4] [45]  Nevertheless, the examples of Spain and the Netherlands, where R&D payments have been exempted from mandatory disclosure on a name basis,  43  suggest limited transparency gains compared to self-regulation. However, shedding more light onto R&D payments has recently been enabled by country-level annual payment summaries published as part of EFPIA's compliance monitoring. 85 Further, eurosfordocs. eu,  86 a new database integrating payments from eleven countries, has enabled comparative company-level analysis. 76, 79 Finally, "methodological notes" accompanying company disclosures in countries with self-regulation  78, 87  may illuminate how the industry interprets code provisions regarding, for example, the demarcation of R&D from non-R&D payments, which should be reported on a name-basis (subject to recipient consent in most countries).
Data Collection We mapped approaches to payment disclosure in European countries by surveying available academic literature in Scopus (search terms: "Sunshine Act, " "Open Payments, " "European Federation of Pharmaceutical Industries and Associations" or "EFPIA" and "disclosure"). The same strategy involved using google searches to find grey literature. Additionally, searches on EFPIA's website and the websites of its trade group members identified their codes of practice, reports, and press releases relating to payment disclosure. We triangulated these sources using country profiles compiled by MediSpend  88  and company disclosure reports and methodologies from the websites of four major companies present in most European countries (Amgen, GSK, Merck Serono, Bayer). In countries with public regulation, we examined legislation, decisions by data protection agencies, legal commentary, websites of regulatory bodies involved in payment disclosure, and disclosure databases.
Finally, between mid-November 2020 and January 2021, we conducted an email survey (see Supplementary file 1). Of 34 approached pharmaceutical industry trade groups, 17 responded, including 14 which answered at least some of the questions. Of 13 approached public or self-regulatory/public bodies, ten replied. Of those, six answered at least some of the questions, three sent holding messages, and one redirected us to another institution. Exceptionally, where data was not available in English we translated it with Google Translate and Deepl. com, and, if necessary, sought clarification from the survey respondents and colleagues with the knowledge of local languages. To identify provisions pertaining directly to R&D payment disclosure we examined the EFPIA Code (countries with self-regulation) as well as legislation, regulatory decisions by data protection agencies, regulatory bodies' websites, and disclosure databases (countries with public regulation or combining public and self-regulation).
Within the former group of countries, we considered a 2019 Europe-wide report collating data from EFPIA trade group members. 81 We sought for any missing information in trade group annual reports and press releases. We also extracted information from databases run by the trade groups in Romania (R&D payments)  93  and the United Kingdom (all payments). 94 Finally, using eurosfordocs. eu we generated summaries of R&D and non-R&D payments reported in Belgium, Ireland, and non-R&D payments in Romania. 95 Finally, we extracted payment data from the responses to the stakeholder survey. We were able to collect country-level R&D data for two countries with unique payment categories introduced by legislation -Slovakia and France. The French data was extracted from eurosfordocs. fr, a database run by a nongovernmental organisation enhancing the accessibility of payments reported in a government-run payments database, Transparence Santé.
The Slovak data was extracted from a database published by Národné Centrum Zdravotníckych Informácií. Finally, we used eurosfordocs. eu to extract companylevel data from three countries, Belgium, Ireland, and the United Kingdom, disclosing R&D payments following EFPIA's disclosure template and having complete data for all companies. 79 To account for year-to year payment variability and multi-year payments, especially relevant for clinical trials, we considered the entire 2017-2019 data series covered by eurosfordocs. eu. 7] [98]  We also reviewed the drug and clinical trial portfolios reported on the websites of the top three companies with the highest R&D payment shares in each country.
75 However, payments associated with retrospective non-intervention studies (eg, reviews of records or databases  75  ) should be recorded within two other, non-R&D, payment categories published on a name basis subject to recipient consent -contributions to costs of events or fees for service or consultancy. 75 The EFPIA Code stipulates that non-intervention studies (prospective or retrospective) "that are not conducted to maintain a marketing authorisation" (in application and following definitions of the "Clinical Trials" Regulation 536/2014), will be disclosed under "consultancy/fee-for-services. " 75 Country a Self-regulation b Public Regulation c Austria ✓ Bosnia & Herzegovina ✓ Belgium ✓ d,e Bulgaria ✓ Croatia ✓ Cyprus ✓ Czech Republic ✓ Denmark ✓ f Estonia ✓ d Finland ✓ d France ✓ Germany ✓ Greece ✓ d Hungary ✓ d Iceland ✓ Ireland ✓ Italy ✓ Latvia ✓ d Lithuania ✓ d Luxembourg ✓ North Macedonia ✓ Malta ✓ The Netherlands ✓ d,e Norway ✓ Poland ✓ Portugal ✓ Romania ✓ d Russia ✓ Serbia ✓ Slovakia ✓ g Slovenia ✓ Spain ✓ d,e Sweden ✓ Switzerland ✓ Turkey ✓ UK ✓ Ukraine ✓ n = 37 n = 32 n = 5 Abbreviation: R&D, research and development.
eu, which provided payment data for Belgium, Ireland, Romania, and the UK, payment values in non-euro currencies were converted to euros using CurrencyConverter,  135  a Python library for exchange rates. Countries are sorted descendingly based on the value of R&D payments. d The statistics for Belgium and the Netherlands cover both the pharmaceutical and medical devices industries. The statistics for other countries cover the pharmaceutical industry only. e In countries adopting self-regulation based on EFPIA's definition of R&D payments, these payments should be related to non-clinical studies, clinical trials, and prospective non-intervention studies. 75  f All disclosed payments include R&D and non-R&D payments. g The population-level data was obtained from a Eurostat news release. The data is reported as of 1 st January 2019 in all countries except for Norway, where it is reported as of January 1, 2018.
Belgian payments database includes both pharmaceutical and medical device manufacturers, and has the largest number of companies (491 versus 140 and 45 in the United Kingdom and Ireland, respectively), many of which made only small payments. For example, the share of companies making R&D payments over €1 000 000 during the three-year period was 7% in Belgium (34 of 491), compared to 38% (53 of 140) in the United Kingdom and 27% (12 of 45) in Ireland. Nevertheless, the concentration by major funders was the highest in Ireland, as demonstrated by the shares of R&D payments held by the top 10% or 20% companies (Table  6 ). Notably, the largest funder, Allergan (subsequently merged with AbbVie), made payments representing 30% of all R&D payments in Ireland. Consistent with the pattern of payment concentration, the share of companies with a limited focus on R&D payments was the highest in Belgium, as shown by the percentage interquartile range values.
Discussion Criticisms of self-regulation of drug company payment disclosure in European countries typically emphasise high levels of non-disclosure by healthcare professionals receiving non-R&D payments. 78, 79, 87, 144, 145 However, we suggest that R&D payments are an even greater challenge to transparency of financial relationships between the pharmaceutical industry and the healthcare sector. Although EFPIA commits, at least rhetorically, to increasing individual consent rates for non-R&D payments,  146, 147  it allows, by default, the recipients of R&D payments to remain undisclosed. Paradoxically, then, non-R&D payments whose recipients refused to be named are more transparent than  Notes. The R&D payment data reported in this table was extracted from eurosfordocs. eu. 86 7] [98]  The years covered by the sales rankings corresponded with the years in which the payment disclosures were made.
Nevertheless, public regulation can be suboptimal, as demonstrated by the cases of Spain, Belgium, and the Netherlands, where the mandatory disclosure of non-R&D payments has not been extended to R&D payments. Further, even in countries mandating disclosure of R&D payments, such as Slovakia, its extent can be diminished indirectly by the scope of funders or recipients falling under disclosure requirements. 43, 44 ata presentation is another challenge. 43, 76 Only in France and Slovakia are R&D payments itemised and available for analysis, although in France it is thanks to an independent disclosure platform, eurosfordocs. fr, offering user-friendly access. Contrastingly, identifying R&D payments in the Danish and Portuguese databases would require scrutinising each payment description. Finally, only in three countries can payment data be connected to other databases -in Denmark and France via recipient identifiers, while in Slovakia -via clinical trial numbers.
Overall, we may have overstated the advantages of public regulation over selfregulation. We could not collect R&D payments from 17 of 32 countries with self-regulation lacking centralised databases. Nevertheless, these countries are unlikely to have payment patterns radically different to those we identified, as demonstrated by cross-country analyses of non-R&D payments. 79 Further, as France and Slovakia had considerably different R&D payment shares, similar differences might exist between the three other countries with public regulation where we were unable to collect R&D payment data.
The payments should be disclosed in a centralised database with an accessible interface, allowing for integration with existing and planned EU databases, via funder, recipient, and activity (eg, clinical trial) identifiers. 43, 76 A centralised disclosure system would match the cross-European nature of today's clinical research, for example, multi-country trials. 167 9] [170]  Given the evidence of the impact of pharmaceutical lobbying at the EU  171  and national levels  153, 172  the legislative process must itself be transparent and involve broad public consultation. Further, R&D payments should be incorporated into existing national disclosure systems, including compulsory registers of interests reported by physicians,  173, 174  authors of treatment guidelines, or members of advisory committees,  175  such as those evaluating technologies applying for public funding.
Data sources:• France -eurosfordocs. fr, an independent online platform which enhances the accessibility of data payment data disclosed in the public database Transparence Santé. •Slovakia-paymentdatabaseavailable from the website of Národné Centrum Zdravotníckych Informácií. 122  bThe French data covers payments made by pharmaceutical companies (manufacturers of "human drugs"). Payments reported by Slovakia also cover payments made by pharmaceutical companies. cThebreakdown of R&D payments presented in the table follows the study types reported in the French and Slovak datasets. dAlldisclosed R&D payments are the sum of specific types of R&D payments. eAllpayments include R&D and non-R&D payments. fThepopulation-level data was obtained from a Eurostat news release. The data is reported as of January 1, 2019.
Acknowledgements We would like to thank all pharmaceutical industry trade groups and public authorities which have responded to our request for information and took the time to explain the nature of local regulatory solutions. We are especially grateful to the  ABPI's Disclosure UK Team  for their continued interest in our research and openness in sharing details of their work. We also thank colleagues who commented on drafts of this paper and offered linguistic assistance:  Marcell Csanadi ,  Eniko Csanadi ,  Alice Fabbri ,  Kevin Jean ,  Fatma Korkmaz ,  Olga Loblova ,  Savvas Morris ,  Emily Rickard ,  Eszter Saghy ,  Christos Vasilakis , and  Weronika Ozieranska.
Country a,b Breakdown of Disclosed R&D Payments c All Disclosed R&D Payments d Study Types €m (% All R&D Payments e ) €m (% All Payments) €m Per m Inhabitants f Data sources:• France -eurosfordocs. fr, an independent online platform which enhances the accessibility of data payment data disclosed in the public database Transparence Santé. •Slovakia-paymentdatabaseavailable from the website of Národné Centrum Zdravotníckych Informácií. 122  bThe French data covers payments made by pharmaceutical companies (manufacturers of "human drugs"). Payments reported by Slovakia also cover payments made by pharmaceutical companies. cThebreakdown of R&D payments presented in the table follows the study types reported in the French and Slovak datasets. dAlldisclosed R&D payments are the sum of specific types of R&D payments.
Background: The European post-authorisation study (EU PAS) register is a repository launched in 2010 by the European Medicines Agency (EMA). All EMA-requested PAS, commonly observational studies, must be recorded in this register. Multidatabase studies (MDS) leveraging secondary data have become an important strategy to conduct PAS in recent years, as reflected by the type of studies registered in the EU PAS register. Objectives: To analyse and describe PAS in the EU PAS register, with focus on MDS. Methods: Studies in the EU PAS register from inception to 31st December 2018 were described concerning transparency, regulatory obligations, scope, study type (e. g. , observational study, clinical trial, survey, systematic review/meta-analysis), study design, type of data collection and target population.
validation purposes, a second revision of key fields for a 15% random sample of studies was carried out by a different centre. The inter-rater reliability (IRR) was then calculated. Finally, to identify predictors of primary data collection-based studies/versus those based on secondary use of healthcare databases) or MDS (vs. non-MDS), odds ratios (OR) and 95% confidence intervals (CI) were calculated fitting univariate logistic regression models. Results: Overall, 1426 studies were identified. Clinical trials (N = 30; 2%), systematic reviews/meta-analyses (N = 16; 1%) and miscellaneous study designs (N = 46; 3%) were much less common than observational studies (N = 1227; 86%). The protocol was available for 63% (N = 360) of 572 observational studies requested by a competent authority. Overall, 36% (N = 446) of observational studies were based fully or partially on primary data collection.
Of 757 observational studies based on secondary use of data alone, 282 (37%) were MDS. Drug utilisation was significantly more common as a study scope in MDS compared to non-MDS studies. The overall percentage agreement among collaborating centres that collected the data concerning study variables was highest for study type (93. 5%) and lowest for type of secondary data (67. 8%). Conclusions: Observational studies were the most common type of studies in the EU PAS register, but 30% used primary data, which is more resource-intensive. Almost half of observational studies using secondary data were MDS. Data recording in the EU PAS register may be improved further, including more widespread availability of study protocols to improve transparency. K E Y W O R D S EU PAS register, multi-database studies, post-authorization studies.