dataesr/scientific-paragraphs-categorization huggingface dataset:
26 r spontaneous reports, it is considered that the report implies a possible causal relationship between the reported event and treatment. 81 However, regulatory authorities and pharmaceutical companies may add a comment on the case report concerning their own assessment of the case. 0] [21]  In these databases differences have been reported between children and adults for the type of the most commonly reported ADRs and the medications most frequently associated with these events. 21, 108, 109 The particularities of paediatric signal detection and causality assessment are described in more detail in Section 2. 7. Registries are common in paediatrics, particularly for rare diseases where they become essential to inform disease course and evaluate potential new therapies or additional indications for existing approved medicines.
Clinical datasets and patient registries provide valuable information on co-medication, medication errors, off-label indications, subgroups which may not benefit, and signals of dependence, all of which are extremely useful to identify participants for case-control studies. Case-control studies evaluate the presence of an exposure in cases (where the outcome of interest is present) and controls (where the outcome is absent). 110 Case-control studies are an efficient use of resources when studying diseases with long latency which would otherwise necessitate many years of follow-up before the outcome occurs (e. g. , cancer). 110 Methodological considerations including the selection of a control group and details on exposure history should be established a priori so as to limit bias. 4 Furthermore, management of confounding bias should acknowledge that researchers cannot control for variables for which they have no data, which often include environmental factors and social determinants of health.
Surveillance studies provide vital insight into clinical uses for approved medicines which can lead to revised marketing authorisations that improve patient safety and encourage access to effective therapies. Surveillance studies should be designed with clear objectives, a prespecified analysis plan, considerations for patient confidentiality, and reflect practical demands on paediatrician workload. There are limitations to both registries and surveillance networks as often the variables collected are prespecified and may not include biological samples to assess the impact of potentially relevant genomic risk factors. [120] [121]  EHRs in general are not adapted for paediatric use, which in part is due to the lack of paediatric functionalities of databases originally created for adults. 122 For example, in the US only 8% of paediatricians use a fully functional paediatric EHR.
32, 129, 134, 135 In addition, children may be treated with several other medications which may have the same excipients as the study drug. 6] [137]  Finally, interactions between different types of excipients may lead to toxicity. 138 Due to the vulnerability of children, it is recommended to aim for formulations with as few excipients as possible and ideally none at all. 129, 135, 139 The current knowledge on excipients in paediatric medicines is available from the Safety and Toxicity of Excipients for Paediatrics (STEP) database. 140 Examples of safety points to consider for excipients in paediatric formulations are listed in Table  S6  in the Supporting Information. Bayesian) are commonly used. 105, 109, 141, 142 Signals for paediatric risks may originate, for example, from health authority or pharmaceutical industry safety databases, clinical trials, observational studies, registries, non-clinical studies, PK/PD studies or the literature.
Challenges of using electronic health records for paediatric safety pharmacoepidemiology studiesPaediatric pharmacoepidemiology safety studies provide important data on the use of medications in a large number of children, help understand risk factors and outcome (short-and long-term) of ADRs and support the assessment of the effectiveness of risk minimisation activities. 117 They use large electronic health record (EHR) or administrative databases or a combination of both. The benefits of linking participants include:• minimising the effects of drop-out• minimising research participant burden• evaluating health care utilisation. It can be challenging, however, to combine EHR and administrative data across jurisdictions as many privacy laws do not allow certain data to leave the region. Coding of paediatric data requires an understanding of how diseases are diagnosed and treated in children.
An additional challenge is that young children and those with developmental delay may not be able to communicate effectively clinical signs of ADRs. This means that parents and health care professionals may need to interpret what the child is trying to express. It is therefore important that standard operating procedures for paediatric data are population-specific and that they have been validated. The validation of paediatric coding algorithms, diagnostic criteria and the control of data quality are recommended for all paediatric studies using EHR and administrative databases. Therefore, whilst the standards of good pharmacoepidemiology practice apply to children, understanding how.
SUMMARYPaediatric pharmacovigilance and drug safety are facing a number of challenges which should be addressed in a collaborative manner by society as a whole as well as relevant key players. For example, pharmaceutical industry and regulatory authorities, with the support of academic researchers, may consider addressing the lack of paediatric formulations and age-group-specific PK and PD data for patentprotected as well as off-patent medicines regardless of their labelling status (i. e. , off-label/unlicensed). The functionality of EHRs, reimbursement records and safety databases should be updated, including databasing and coding processes and data extraction, to suit the needs of paediatric patients. This should include the option of linking to the health records of the parents and siblings where indicated.
2. 5. 3 | 53 Challenges of using electronic health records for paediatric safety pharmacoepidemiology studiesPaediatric pharmacoepidemiology safety studies provide important data on the use of medications in a large number of children, help understand risk factors and outcome (short-and long-term) of ADRs and support the assessment of the effectiveness of risk minimisation activities. 117 They use large electronic health record (EHR) or administrative databases or a combination of both. The benefits of linking participants include:• minimising the effects of drop-out• minimising research participant burden• evaluating health care utilisation. It can be challenging, however, to combine EHR and administrative data across jurisdictions as many privacy laws do not allow certain data to leave the region.
Coding of paediatric data requires an understanding of how diseases are diagnosed and treated in children. An additional challenge is that young children and those with developmental delay may not be able to communicate effectively clinical signs of ADRs. This means that parents and health care professionals may need to interpret what the child is trying to express. It is therefore important that standard operating procedures for paediatric data are population-specific and that they have been validated. The validation of paediatric coding algorithms, diagnostic criteria and the control of data quality are recommended for all paediatric studies using EHR and administrative databases. Therefore, whilst the standards of good pharmacoepidemiology practice apply to children, understanding how.
3 | 3 SUMMARYPaediatric pharmacovigilance and drug safety are facing a number of challenges which should be addressed in a collaborative manner by society as a whole as well as relevant key players. For example, pharmaceutical industry and regulatory authorities, with the support of academic researchers, may consider addressing the lack of paediatric formulations and age-group-specific PK and PD data for patentprotected as well as off-patent medicines regardless of their labelling status (i. e. , off-label/unlicensed). The functionality of EHRs, reimbursement records and safety databases should be updated, including databasing and coding processes and data extraction, to suit the needs of paediatric patients. This should include the option of linking to the health records of the parents and siblings where indicated.
g. , type of co-medications) • Be aware of increased risk of medication errors from the use of co-medications (including off-label/unlicensed) and what type of ADRs this may cause (e. g. , accidental overdose, lack of efficacy) • medications Clinical safety data (i. e. , clinical trials, spontaneous reports, observational studies) • Analyse pooled safety data (all age groups +/À indications) • Stratify data by age groups and other factors (e. g. , indications, dose, risk factors for ADRs) • Explore data from large safety databases (e. g. , VigiBase, EudraVigilance, FAERS) Pharmacoepidemiological data • Is there drug utilisation data for children? • Is there pharmacoepidemiological safety data for adults and children providing more insight into risk factors, outcome and, where applicable, the effectiveness of any risk minimisation activities? • Consider the limitations of paediatric electronic health records and administrative databases (see Section 2.
Acknowledgements We thank  Drs. Julie Dam ,  Erika Cecon  and  Lei Liu  ( Institut Cochin, Paris ) for careful reading of the manuscript. This work was supported by the  Fondation de la Recherche Médicale  (Equipe FRM  DEQ20130326503 ),  Agence Nationale de la Recherche  ( ANR-2011-BSV1-012-01  " MLT2D " and ANR-2011-META " MELA-BETES ),.
Mass spectrometry analysis Metabolomics data were acquired by liquid chromatography-high resolution mass spectrometry (LC-HRMS) using previously published methods  [20]  that enabled detection of hydrophilic metabolites. Urine dilution was normalised to specific gravity prior to data acquisition  [21]. Detailed methods are provided in the supplementary material. Tryptophan and 6 of its metabolites were quantified by reversed-phase liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) as described in the supplementary material.
Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway mapping was performed using gene set variation analysis (GSVA) enrichment score (ES) determination of the genes identified as being part of the fatty acid metabolism pathway. The Type-2 patient stratification was based on the ES of the IL-13-induced gene expression patterns in human bronchial epithelial cells using GSVA  [23, 27] , and transcriptome-associated cluster (TAC) membership was assigned based upon previous work  [23]. Participant clinical and biochemical data were collected from the U-BIOPRED TranSMART platform (eTRIKS). All statistical analyses were performed using MATLAB (Mathworks, Natick, MA, USA).
Data were acquired using a 1290 Infinity II ultra-high performance liquid chromatography system coupled to an Agilent 6550 iFunnel Q-TOF mass spectrometer (Agilent Technologies, Santa Clara, CA, USA). Metabolites were separated using hydrophilic interaction liquid chromatography (SeQuant ZIC-HILIC column 100 Å, 100 × 2. 1 mm, 3. 5 μm particle size) coupled to a 2. 1 × 2 mm, 3. 5 μm particle size guard column (Merck, Darmstadt, Germany) and an inline-filter. Mass spectral data were acquired in both electrospray ionisation (ESI) positive and negative modes. The mobile phases for ESI-positive ionization mode were water containing 0. 1% formic acid (solvent A) and acetonitrile containing 0. 1% formic acid (solvent B), and for ESI-negative ionization mode were 10 mM ammonium acetate pH 6. 7 (solvent A) and acetonitrile (solvent B).
The elution gradient was as follows: 1. 5 min at 95% [B], 95 to 40% [B] in 12 min, maintained at 40% [B] for 2 min, then decreasing to 25% [B] at 14. 2 min, maintained for 2. 8 min, then returned to initial conditions over 1 min, and then the column was equilibrated at initial conditions for 7 min. The flow rate was 0. 3 mL/min; injection volume was 2 μL, and the column oven was maintained at 25 °C Data were acquired in a mass range of 40-1200 m/z using the following settings: sheath gas, N2, 8 L/min; drying gas, N2, 15 L/min; gas temperature, 250°C; nebulizer pressure, 35 psi; voltage, 3000 V; fragmentor voltage, 380 V. All data were acquired using all ions fragmentation (AIF) mode; this included three sequential experiments at three alternating collision energies (0 eV, 10 eV, and 30 eV).
The data acquisition rate was 6 scans/s. Peak deconvolution and metabolite identification were performed using Agilent TOF-Quant software (version B. 07. 00, Agilent Technologies) as described  [3]. To ensure accurate metabolite identification, metabolites were matched against retention time, accurate mass, and MS/MS fragmentation patterns of 408 chemical reference standards in an in-house database. Metabolites were only included for statistical analysis if the accurate mass, retention time, and MS/MS fragmentation pattern matched to an authentic standard; thus, all metabolites reported have a Level 1 identification level as defined by the Metabolite Standards Initiative  [7]. Systematic experimental within-and between-batch variation was corrected using the QC-Robust Spline Correction (QC-RSC) algorithm  [8].
5 ml/min flow rate, 40°C column oven) using 0. 1 % formic acid in HPLC water (mobile phase A) (Milli-Q, Millipore) and 0. 1 % formic acid in HPLC acetonitrile (mobile phase B) (Rathburn Chemicals) was applied. The gradient started at 2 % B increasing to 40 % B after 2 min and directly to a washing step at 95 % B for 0. 6 minutes and then returned to initial conditions followed by an 0. 8-minute column re-equilibration. Mass spectrometry data (MRM, multiple reaction monitoring) were acquired in positive electrospray ionization mode, using the transitions 225. 09>109. 9, 209. 09>146. 0, 205. 1>188. 1, 192. 07>145. 9, 190. 05>144. 0, 177. 1>160. 0 and 168. 03>105. 9 for 3-hydroxykynurenine, kyrunenine, tryptophan, 5hydroxyindoleacetic acid, kynurenic acid, serotonin and quinolinic acid, respectively.
Fragmentor voltage was set to 380 V and the Collision energies ranged from 10 to 18 V. In positive mode, the capillary voltage was 4. 0 kV with a sheath gas temperature of 400°C and gas flow of 12 l/min. The Ion Funnel parameters were 200 and 110 for the high and low pressure radio frequencies. Data handling and quantification was performed using Agilent MassHunter B06. 00 software. Samples were randomized across each batch to prevent potential confounding signal drift.
Genotyping Sputum and bronchial brushing cis-eQTL summary statistic data were obtained in U-BIOPRED. The U-BIOPRED genotype data was imputed by IMPUTE2  [9]  using 1000Genome phase 3 data  [10]  as the reference panel. The sputum eQTL analysis was performed on 91 U-BIOPRED participants that had both genetic and gene expression data in sputum available, and the bronchial brushing eQTL analysis was performed on 118 U-BIOPRED participants that had both genetic and gene expression data in bronchial brushings available. The eQTL analysis was performed with matrixEQTL in R  [11]  using age, sex and 10 principle components as covariates. The asthma GWAS summary statistics were also downloaded from a recent large scale GWAS study  [12]. The number of cases and controls in the adult-onset GWAS were 26,582 and 327,253, respectively.
Confounder correction was performed on the log urinary carnitine abundances using multiple linear regression, adjusting the clinical outcome for sex, age, and BMI. To provide robustness against heteroscedasticity, Huber's sandwich estimator for the regression coefficient standard errors was used  [17]. Collection sitespecific batch effects upon the observed metabotypes were evaluated and found to vary with metabolite. To identify similarities between metabolites, hierarchical cluster analysis (HCA) was performed using a multivariate Spearman correlation distance metric and Ward's group linkage. The most similar metabolites form the lowest linkages in the resulting circular dendrogram; thus, emergent clusters represent similar trends. The mean of the logtransformed and z-scaled data of the resulting clusters were plotted against clinical groups to qualitatively visualise metabolite patterns across clinical groups.
We used a p-value threshold 0. 05 here without considering the multiple testing correction due to the smaller sample size, and we have therefore emphasised caution when interpreting the result. Individual subject clinical and biochemical data were collected from the U-BIOPRED TranSMART platform (eTRIKS). All statistical analyses were performed using the MATLAB scripting language (Mathworks, Natick, MA, USA), R (3. 4. 4, CRAN Network), and STATA v14 (StataCorp LLC, College Station, TX, USA).
Funding The  U-BIOPRED  consortium received funding from the  European Union  and from the  European Federation of Pharmaceutical Industries and Associations  as an  IMI JU  funded project (no. 115010 ). Grants were also received from the  Swedish Heart-Lung Foundation, Swedish Research Council  ( 2016-02798 ,  2014-3281 ,  2016-0338 ), the  Konsul Th C Berghs research foundation , the  ChAMP (Centre for Allergy Research Highlights Asthma Markers of Phenotype) consortium , which is funded by the  Swedish Foundation for Strategic Research , the  Karolinska Institutet ,  AstraZeneca & Science for Life Laboratory Joint Research Collaboration , and the  Vårdal Foundation. SN Reinke was supported by the  Canadian Institutes of Health Research  ( MFE-135481 ). CE Wheelock was supported by the  Swedish Heart Lung Foundation  ( HLF 20180290 ).
The running distance along the slide was measured 10 s after the gel placement. Gel leakage, expressed as the distance traveled by the gel, was measured three times, and the mean values ± standard deviations were calculated. For the in vitro adhesion test, 50 mg of colored LO, PG, and PLO-3 were placed at the center of the agar slides. The agar slides were immersed in 10 mL of phosphate buffer pH 7. 4 at 37 ± 1 • C for 2 h. The formulation residence times on the slides were visually compared after 10 and 60 min, measuring the increase in the area occupied by the gels using ImageJ software  [34]. The tests were performed three times. An in vivo skin adhesion test was performed on a volunteer, applying 30-40 mg of colored LO, PLO-3, and PG on the inner part of the forearm rotated outward. Forearm images were taken immediately after gel application, and 1 min or 10 min after rotating the forearm inward in vertical position.
INTRODUCTION Spontaneous reports are a valuable data source to complete the pre-marketing safety profile of medical products, notably for rare or long latency adverse drug reactions (ADRs)  (Hartmann et al. , 1999; Hauben and Bate, 2009). With the massive increase in new reports received each year by pharmacovigilance centers around the world (e. g. more than 2,700,000 new reports were recorded in the WHO's pharmacovigilance database in 2019), traditional approaches such as manual review needed to be complemented. Automated screening tools using quantitative methods have thus been developed to detect signals of disproportionate reporting (SDR), i. e. a higher proportions of reporting of ADRs for a studied drug as compared to the other drugs in the database. They allow broad screening to trigger further investigations, to detect more complex dependencies and to prioritize potential signals  (Bate and Evans, 2009; Hauben and Aronson, 2009).
Data Source All data used for disproportionality analyses were extracted from the WHO pharmacovigilance database, VigiBase, from January 1, 1968 to December 31, 2019. Gathering reports from more than 130 member countries, Vigibase is the largest pharmacovigilance database containing more than 21 million individual case safety reports (ICSRs) submitted by pharmaceutical manufacturers, health professionals, or consumers through national pharmacovigilance systems  (Lindquist, 2008). We identified the four outcomes in Vigibase by using a collection of MedDRA Preferred Terms (PT) or standardized MedDRA queries (SMQ) to match the broader definitions used in the reference set (Supplementary Table  S1 ). (Reich et al. , 2013; Ryan et al. , 2013).
pdf, 2006). We used seven disproportionality models for which the results were very unlikely to be influenced by actual differences in absolute risks of ADR: Model 1: only suspect reports included; Model 2: to assess the influence of pharmacovigilance systems between countries we restricted reports to a specific country (United States); Model 3: restricting reports to those submitted by health care professionals only (physicians or pharmacists); Model 4: restricting the database to the drug's corresponding therapeutic area (ATC code level 3) to account for difference non-cases populations; Model 5: including only serious cases; Model 6: included only cases reported within 5 years after the drug's marketing approval date to account for reporting variability according drug time on the market; and Model 7: included suspected and concomitant drugs.
Data and Signal Generation Results The distribution of included cases in each analysis and the results of disproportionality analyses for the seven models are presented in Figure  1  and Table  1  respectively. Over the 399 drug-event pairs, signals could not be examined for four drugs relating to thirteen events in the reference dataset (3. 26% of the set) as these drugs were not found in VigiBase. As recommended, ROR values were not computed when the number of exposed cases was lower than 3. This led to lack of ROR value for 4. 8-51. 9% of the 399 drug-event pairs for the model including concomitant drugs and the model including only ICSRs within 5 years of the drug's approval, respectively. The ROR LB values varied from 0. 01 (gastrointestinal bleeding and neostigmine) to 57. 6 (acute liver injury and propylthiouracil) and IC LB values from -17.
In 2013 Harpaz et al. compared the performances of several disproportionality methods using the FDA adverse event reporting database using the same reference set  (Harpaz et al. , 2013). They also found a better specificity than sensitivity of the reported odds ratio but the overall performances were inferior to those in our study. That may be due to the greater number of cases included in our study (21 million vs. 5 million) and broader definitions of outcomes, consistent with other studies that found higher performances using larger databases and similar event definitions  (Reich et al. , 2013; Caster et al. , 2020). Our study had some limitations. The reference dataset used here is one of the largest to date that incorporates both SPCs and evidence from literature reviews. However, Hauben et al.
suggested that a small part of false drug-event pairs may be misclassified, which could impact the performances of disproportionality analyses  (Hauben et al. , 2016). Nonetheless, the comparisons between models and methods are unlikely to have been affected by this bias. Moreover, to standardize the models we had to make choices (e. g. 5 year after drug approval for model 6, United States country for model 2 or defining the therapeutic area by the ATC level 3 in model 4). These standardized definitions may not be relevant for some drugevent pairs (e. g. for drugs not commercialized in the United States or belonging to an heterogenous ATC class) and have to be further adapted to the nature of the studied drug and ADR. The findings of our study may not be generalizable to other databases due to differences related to the database background and the medical products covered.