dataesr/scientific-paragraphs-categorization huggingface dataset:
Key Points • The present study provides a detailed description of all studies registered in the European post-authorisation study (EU PAS) register from its inception till the end of 2018, focusing on various aspects of study design and multiple database studies specifically. • Our results showed a steady increase in the number of observational studies registered, providing the most recent updated review of the EU PAS register. • We leveraging a network of pharmacoepidemiologists from various centres belonging to European Network of Centres for Pharmacoepidemiology and Pharmacovigilance to collect additional and detailed information on methodological aspects of observational studies. • The present study is the only one that evaluates the inter-rater reliability of different investigators during the data collection process, thus providing indirect measure of the completeness and accuracy of the data collected.
Slightly more PASS used primary data collection (i. e. , data prospectively collected for the purpose of the study) except those studies assessing drug utilisation where secondary use of already existing healthcare data was leveraged. Another review, conducted by Carroll et al and focusing on studies available on the EU PAS register as of October 2016,  4  corroborated that primary data collection was more common in studies aimed at assessing safety and effectiveness and less common when assessing drug utilisation. In 2018, Vora et al and Farcas et al,  5, 6  used the EU PAS register to explore specifically studies evaluating the implementation and impact of risk minimisation measures. All these previously conducted EU PAS register-based studies relied primary on the data reported within the register, without validation of the collected information or additional information on methodological aspects based on expert review.
Another gap in these previous studies is the lack of focus on multiple-database studies (MDS), which are observational studies conducted using more than one source of routinely collected data (e. g. , claims database, electronic health records [EHRs]). MDS are of particular importance because they allow the accrual of a very large cohort of patients, which is of particular relevance to paediatric populations and rare diseases, as well as several other populations of special interest. Indeed, the number of MDS has been increasing over the years. Since EU PAS register is a platform for the mandatory recording of data on observational studies as per EU legislation, identifying and describing such studies and their impact on the landscape of observational research is therefore of great value as this has never been done to our knowledge.
7 ven how quickly observational research is growing, the aim of the present descriptive study was to conduct an updated and detailed review of the studies that were registered in the EU PAS register from its inception till the end of 2018, providing additional information on data type (e. g. , distinguishing between claims data, EHRs, etc. ) and study design (e. g. , distinguishing between descriptive studies, cohort studies, casecontrol studies, etc. ). Another aim of this study was to conduct an assessment of the inter-rater reliability following the collection of data.
| Data collection A dataset containing all studies found in the EU PAS register from its inception to 31st December 2018 was provided by the EMA. The EU PAS register is publicly accessible online (http://www. encepp. eu/ encepp/studySearch. htm). The data collection was carried out independently by 34 investigators from 13 academic centres or contract research organisations being part of ENCePP on common and detailed instructions and using the same electronic case report form (Figure  1 ). The resulting dataset contained data from the EU PAS register concerning different aspects of study transparency (ENCePP Seal, protocol and availability of publication), regulatory obligations, methodology, target population, scope and drug under study (chemically synthetised vs. biological drugs, orphan drugs).
EU PAS register data was supplemented by retrieving data from study protocols or publications, if available, on source of funding, whether a study was an MDS (defined as a study using more than 1 source of already existing databases which could not be linked at patient level), study design, use of reference drug for formal comparison (if any). The full protocol for data collection, including the fields provided by the EMA, is provided in Appendix S1A. Once all the data was collected it was harmonised based on pre-defined criteria. To evaluate how consistent data collection was, data retrieved by investigators from a centre was checked by investigators from another centre for a 15% random sample of studies using the same protocol used for the main data collection; each investigator validated five studies.
| Statistical analysis The cumulative frequency of study registration in the EU PAS register was plotted. This was stratified by type of study, data collection and by MDS or non-MDS status specifically. An overview of all studies was provided using descriptive statistics. This was done by stratifying at a high level by type of study (classified as clinical trials, observational studies, systematic review/meta-analysis, questionnaire-based surveys and others). Finally, odds ratios (OR) and 95% confidence intervals (CIs) were calculated fitting univariate logistic regression models to investigate whether study-related variables (e. g. , study type, data source, etc. ) were associated with the use of primary data versus use of secondary data as a (reference) and whether they were associated with non-MDS versus MDS (reference) as a reference.
To better understand the data in the EU PAS register and add value to that data with the inclusion of further information related to methodology a large number of investigators was involved for collecting data. Cohen's Kappa statistic was calculated in order to evaluate the inter-rater reliability (IRR). This was done by single variables and macro-categories consisting of several variables. Kappa estimates and their 95% CI were obtained by the resampling bootstrap method to account for heterogeneity between academic centres. Bootstrap replicate number was set equal to 100 000.
| RESULTS After excluding two duplicates, a total of 1426 PASs were identified in the EU PAS register from its inception to 31st December 2018. The majority of studies were observational (N = 1227; 86. 0%). Almost a quarter of all observational studies were based on primary data collection (N = 299; 24. 4%; Figure  2 ). Two-thirds of observational studies were based only on secondary use of existing healthcare data, that is, data not collected primarily for research purposes (N = 757; 61. 7%). N = 299 (24. 4%) were based on primary data and N = 147 (12. 0%) were based on both primary data collection and secondary use of databases. The frequency of studies registered in the EU PAS register showed a steady increase in the number of observational studies from 2010 to 2016, while a slight decrease was observed during the last 2 years under study (Figure  3 ).
3%) observational studies and it was classified as 'non-EU RMP only' for 102 (8. 3%) of them. The scope of most observational studies was risk assessment (56. 7%), followed by drug utilisation (36. 2%). A large proportion of observational studies was conducted included the elderly (82. 2%). Only 36. 0% (N = 442) of observational studies were conducted included children, and not all of these studies included children exclusively. With regards to drugs of interest, biologic drugs were studied in 24. 3% of all observational studies; orphan drugs were the focus of 12. 8% of observational studies. Concerning the type of secondary data used, among observational studies the use of claims, EHRs or existing registries was similar, at 13. 4, 14. 9 and 11. 1%, respectively (Figure  4 ). The most commonly used study design among observational studies was the cohort study (52.
In general, there was no substantial difference in study design between studies based on primary data collection or secondary use of data, although descriptive studies were slightly more common in the former. Only a third of all claims-and EHR-based observational studies were requested by a regulator (Table  4 ). Claims data were more commonly used for risk assessment than EHRs (60. 8% vs. 40. 7%). Among observational studies, as compared to non-MDS, MDS were more likely to have a protocol deposited in the EU PAS register (OR: 1. 9; 95% CI: 1. 4-2. 5), to have an ENCePP Seal (OR: 3. 0; 1. 9-4. 7), to be funded by national/international drug agencies (OR: 1. 9; 95% CI: 1. 1-3. 4) and to include children as study population (OR: 2. 1; 95% CI: 1. 5-2. 6). Moreover, almost two-thirds of MDS were requested by a regulator.
| DISCUSSION The present study provides a detailed description of all studies registered in the EU PAS register from its inception until the end of 2018, focusing and providing detail on various aspects of study design and multiple database studies specifically. As expected, as compared to the most recent review of EU PAS register available,  4    providing indirect measure of the completeness and accuracy of the data collected in the EU PAS register. From our study, it appears that only 61. 7% of observational studies were conducted through the secondary use of existing healthcare data. This finding indicates that the remaining one third of observational studies were conducted using the much more resourceintensive method of collecting data ad hoc, that is, primary data collection. It is not known whether such studies could have been easily conducted using routinely collected healthcare data, which provide detailed information on population characteristics, exposure and outcome.
In general, we expected fewer studies to have been conducted using primary data, as large use of primary data source can reflect the need to improve the access to data for research purpose. In line with previous studies, primary data were more commonly used for effectiveness and risk assessment studies,  3, 4  while secondary data was more likely to be used for drug utilisation studies and risk assessment studies. The increase in use secondary data source was sharpest in the last 2 years, while until 2016 the use of primary data source was in in 56% of the 316 studies identified in the EU PAS register. 4  note, some markers of high ethical standards and transparency such as protocol deposition are often missing. This result has to be The ENCePP Seal identifies studies in the register that adhere to the entirety of the Code's provisions, including timelines for the submission of documents to the ENCePP Secretariat.
However, obtaining this Seal is associated with an added bureaucratic burden that researchers may not be willing to undertake. The usefulness of the ENCePP Seal on a broader scale may be somewhat limited if so few studies undertake the procedures to obtain it. Other initiatives such as the revision of the ENCePP Code of Conduct have recently been undertaken but it is not known to what extent this is being adopted. 10 other interesting finding of this study with important implications concerns the level of agreement seen between pharmacoepidemiologists in the process of data collection from the EU PAS register. While there was substantial agreement for study type, data collection, drug type and use of reference drug for formal comparison, there was only moderate agreement for the rest of the fields, including study setting, classification of secondary data, whether the study was an MDS and whether the study drug was an orphan drug.
High agreement was not reached for any of the fields, due to ambiguous, missing or conflicting reporting. F I G U R E These findings are thought provoking and suggest that data in the EU PAS register was not always clear and complete. Indeed, the quality of data entry in the EU PAS register is not monitored at all as with other similar platforms such as clinicaltrials. gov. In the latter, National Library of Medicine (NLM) staff conducts a review of registered study records for obvious errors or inconsistencies, with important issues being communicated directly to the investigators. 11 However, this does not guarantee complete and accurate records for all studies. 12 In the EU PAS register, it is completely up to the investigator's discretion to conduct data entry correctly and accurately as no quality control is conducted.
The very small number of duplicate studies identified, two, is potentially an indicator that in this sense, the register's data is of satisfactory quality. T A B L E 3 (Continued) EU RMP 2 instructions. These instructions were shared, agreed and made accessible and available among all reviewers, to avoid potential errors in the classification method. Again, to further check, blind quality control of data entry was conducted for some fields, to obtain an overall agreement. In addition, this is the first time that an evaluation of the MDS landscape has been attempted. However, this study also has some limitations. The most important limitation concerns the accuracy of the data collection, which was sometimes limited by the lack of information recorded in the EU PAS register or lack of clarity of such information.
The lack of data and ambiguities in classification of some studies may limit the robustness of our analyses. In these instances, the judgement on categorising characteristics of a study was somewhat subjective. This is a limitation inherent to the way that data is collected in the EU PAS register and to the lack of quality control of such data. Furthermore, since the registration of a study is voluntary, the EU PAS register is not representative of the whole landscape of studies in Europe and some.
our results showed a steady increase in the number of observational studies registered. The present study adds to the available literature by providing the most recent updated review of the EU PAS register, including all F I G U R E 3 Cumulative frequency of studies recorded in the EU PAS register over time. The study by Carrol et al, is included as a benchmark of the most recent overview of the EU PAS register, including however only studies requested by the European Medicines Agency. MDS, multiple database studies; EU PASs, European post-authorization studies [Colour figure can be viewed at wileyonlinelibrary. com] T A B L E 1 Description of all studies identified in the EU PAS register Clinical trials N = 30 (%) Observational studies N = 1227 (%) Systematic reviews/metaanalyses N = 16 (%) Questionnaire-based surveys N = 107 (%) N (%) N (%) N (%) N (%).
4bbreviations: 95% CI, 95% confidence intervals; MDS, multiple database study; OR, odds ratio. studiesmayhave not been captured. Another limitation is that the data was collected only until 2018. However, we hold that the present study still has the added value of providing an overview of the EU PAS register compared to the most recently published reviews, which described studies until 2016. 45| CONCLUSIONObservational studies were the most common type of studies in the EU PAS register, but almost one third of observational studies used primary data, which is more resource-intensive. One quarter of observational studies were MDS. MDS hold untapped potential to investigate special populations, such as patients with rare diseases and paediatric patients. A detailed analysis of IRR during data collection suggested information recorded in the EU PAS register is often not clear or complete.
9) study Not applicable 0. 509 Secondary data Other type of descriptive Chart abstraction 43 (29. 3) 0. 481 89 (29. 8) 145 (67. 8) 0. 501 172 (22. 7) Moderate agreement 1. 4 (1. 0-1. 9) studies Claims database 0. 131 Other types of analytic EHR 7 (4. 8) 0. 457 15 (5. 0) 37 (4. 9) 1. 0 (0. 5-1. 9) studies Existing registry 0. 505 More than 1 study design Not applicable-not 15 (10. 2) 0. 728 2 (0. 7) 52 (6. 9) 0. (0. 0-0. 3) secondary data 3 (2. 0) increase the standards of pharmacoepidemiological research in Unknown 6 (2. 0) 7 (0. 9) - More than 1 Use of reference drug for formal comparison Europe. 9 Competent authorities should keep emphasising the impor-0. 579 Unknown 32 (21. 8) tance of study registration in order to increase the level of transpar-0. 314 Yes 46 (15. 4) 255 (33. 7) 0. 3 (0.
2-0. 5) Multiple database study No ency in the research landscape, particularly for publically funded Yes 0. 503 110 (74. 8) 242 (80. 9) 176 (77. 6) research. Another measure of high ethical standards and transparency, 0. 485 475 (62. 7) - Moderate agreement No 5 (3. 4) the ENCePP Seal, was only available in 7% of all observation studies. 0. 478 Unknown 11 (3. 7) 27 (3. 6) - Setting Unknown 0. 274 Orphan drug Routine Yes 140 (95. 2) 0. 478 179 (83. 6) 282 (94. 3) 0. 453 740 (97. 8) Moderate agreement 0. 4 (0. 2-0. 7) Experimental No 3 (2. 0) 0. 422 7 (2. 3) 11 (1. 5) 1. 6 (0. 6-4. 2) Unknown Unknown 2 (1. 4) - 9 (3. 0) 1 (0. 1) - Note: -, empty cell in the dataset. Not applicable 2 (1. 4) 1 (0. 3) 5 (0. 7) - a Kappa result be interpreted as follows: ≤0 no agreement and 0. 01-0. 20 as none to slight, 0.
Description of specific types of mixed and secondary data EHRs Chart abstraction Claims database N = 189 Existing registry More than one type of data N = 63 (%) N = 171 (%) (%) N = 166 (%) N = 290 (%) N (%) N (%) N (%) N (%) N (%) Protocol deposited Yes 36 (57. 1) 84 (49. 1) 126 (66. 7) 103 (62. 0) 209 (72. 1) No 27 (42. 9) 87 (50. 9) 63 (33. 3) 63 (38. 0) 81 (27. 9) Requested by a regulator Yes 32 (51. 1) 55 (32. 2) 64 (33. 9) 67 (46. 4) 150 (51. 7) No 31 (49. 2) 115 (67. 3) 119 (63. 0) 86 (51. 8) 133 (45. 9) Unknown 0 (0. 0) 1 (0. 6) 2 (3. 2) 3 (1. 8) 7 (2. 4) Source of funding Pharmaceutical company 56 (88. 9) 134 (78. 4) 138 (73. 0) 135 (81. 3) 218 (75. 2) National/international drug agency 2 (3. 2) 10 (5. 8) 17 (9. 0) 9 (5. 4) 12 (4. 1) Public entities excluding drug 4 (6. 3) 12 (7. 0) 10 (5.
Multiple database studies versus non-multiple database studies. MSD was considered the comparator T A B L E 4 (Continued) T A B L E 5 MDS N = 319 (%) EHRs Non-MDS N = 864 (%) OR (95% CI) Protocol deposited Chart abstraction N = 63 (%) Claims database N = 171 (%) N = 189 (%) Existing registry N = 166 (%) More than one type of data N = 290 (%) N (%) N (%) 222 (69. 6) N (%) N (%) 492 (56. 9) N (%) 1. 9 (1. 4-2. 5) Use of reference drug for formal comparison EnCePP seal Yes 7 (11. 1) 86 (50. 3) 42 (13. 4) 47 (24. 9) 43 (4. 9) 45 (27. 1) 97 (33. 4) 3. 0 (1. 9-4. 7) No Requested by a regulator 52 (82. 5) 73 (42. 7) 138 (73. 0) 114 (68. 7) 187 (64. 5) Unknown 4 (6. 3) 12 (7. 0) 184 (57. 7) 4 (2. 1) 7 (4. 2) 372 (43. 0) 6 (2. 1) 1. 6 (1. 2-1. 9) Scope of the study Status Disease epidemiology Planned 6 (9.
8) - -12 (7. 0) -18 (9. 5) -22 (13. 3) 298 (34. 5) -29 (10. 0) - data Unknown Secondary 3 (4. 8) 7 (4. 1) 282 (90. 3) 5 (2. 6) 7 (4. 2) 452 (52. 3) 10 (3. 4) 6. 9 (4. 8-10. 1) Study-specific local data extraction Drug of interest-orphan drugs Mixed - -35 (11. 2) - -108 (12. 5) - 0. 9 (0. 6-1. 3) in a common data model and Yes Unknown 8 (12. 7) 14 (8. 2) 2 (0. 6) 16 (8. 5) 6 (0. 7) 31 (18. 7)) 20 (6. 9) - central analysis No Secondary data 53 (84. 1) 147 (86. 0) 168 (88. 9) 129 (77. 7) 258 (89. 0) General local data extraction in a common data model and central Unknown Claims database -2 (3. 2) -10 (5. 8) 26 (8. 3) -5 (2. 6) -6 (3. 6) 132 (15. 2) -12 (4. 1) 0. 5 (0. 3-0. 7) analysis Publication available Product lifecycle Not applicable Yes Post-marketing high transparency. Orphan drugs were not commonly investigated 36 (57.
Children and youth treated with antipsychotic drugs (APs) are particularly vulnerable to adverse drug reactions (ADRs) and prone to poor treatment response. In particular, interindividual variations in drug exposure can result from differential metabolism of APs by cytochromes, subject to genetic polymorphism. CYP1A2 is pivotal in the metabolism of the APs olanzapine, clozapine, and loxapine, whose safety profile warrants caution. We aimed to shed some light on the pharmacogenetic profiles possibly associated with these drugs' ADRs and loss of efficacy in children and youth. We conducted a systematic review relying on four databases, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 recommendations and checklist, with a quality assessment. Our research yielded 32 publications.
Two authors (D. M. and A. O. G. ) separately conducted the research in PubMed, EMBASE, PsycINFO, and PsycArticles. Our query retrieved publications registered in the four selected databases up to 21 March 2022. Relying on four electronic bibliographic databases, our extraction retrieved for each publication the source database, publication year, language, full list of authors' names, article title, DOI (Digital Object Information), journal title, abstract, and Medical Subject Headings (MeSH) terms associated. Two authors independently performed the preliminary two steps of proper article screening, with the results shown in the PRISMA flowchart (Figure  1 ). Before screening, duplicates were removed. First, the eligibility of the titles and abstracts of the articles identified by the initial query were checked.
Next, full-text copies of the articles whose titles and abstracts met the inclusion criteria were retrieved. Then, to ensure compliance with the inclusion criteria, the yielded full-text articles were assessed for eligibility. When the two reviewing authors could not obtain a consensus regarding an article, the disagreement was resolved through discussion. Lastly, data extraction was performed for all publications that met the inclusion criteria, including the study site(s), study type, characteristics of the subjects (age, sample size, sex distribution, ancestry, diagnosis), antipsychotic(s) of interest and its (their) dosing, other drugs administered, outcome(s) measured, gene variants assessed, their potential association(s) with the ADR(s), the pathophysiology involved, and the pharmacogenetic approach.
For quality assessment needs, we also extracted data addressing the reasons for choosing the genes/SNPs to genotype (summaries of previous findings, reasons given for choosing the genes and SNPs genotyped, the adjustment methods for multiple testing, and the p-values provided for the associations), the sample size (details on calculation of sample size and on a priori power to detect effect sizes of varying degrees), the reliability of genotypes (description of the genotyping procedure, of the primers and of any quality control methods, previously reported genotype frequencies, blind of genotyping personnel to outcome status), missing genotype data (the extent and reasons for missing data, any checks for missingness at random performed, any imputation of missing genotype data, number of patients contributing to each analysis and consistence with sample size), population stratification (tests undertaken for cryptic population stratification and adjustment for in the analyses), Hardy-Weinberg Equilibrium testing (was it performed, and were deviating (or not) SNPs highlighted and excluded from further analysis where appropriate), and choice and definition of outcomes (clear definition of all outcomes investigated, justification, results shown).
Study Selection Selection and progressive elimination of the identified articles are summarized in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flowchart provided in Figure  1. Our database query retrieved 406 records. Before screening, we removed 55 duplicates (see Methods). Then, 352 records were screened on the basis of their title and abstract. Among them, 72 publications were assessed for eligibility via the analysis of their full-text version. Finally, 32 records met the inclusion criteria of this systematic review.