dataesr/scientific-paragraphs-categorization huggingface dataset:
Background A systematic review published in 2014  [1]  identified 198 published core outcome sets (COS) and a recent update found that this figure had increased to 227 by the end of that year  [2]. The details of these COS, along with others that are planned and in development, are recorded in the COMET (Core Outcome Measures in Effectiveness Trials) database. As the number of COS grows, it is important to assess their uptake by clinical trialists because the continued development of COS, without their implementation, could add to waste in research, and would mean that those using the results of trials to make decisions about healthcare will not realise the benefits that using COS can provide. In January 2012 the guidance for NIHR HTA funding recommended 'details should include justification of the use of outcome measures where a legitimate choice exists between alternatives.
Method The completed application form and detailed project description of each NIHR HTA application will be examined for: Evidence that the COMET database had been searched to establish whether or not a COS exists Reference to a COS study published in the COMET database Evidence that a COS was included in the application if one exists Evidence that a COS was not included in the application where one exists Reasons given for not including a COS where one exists Rationale for outcome choice in the absence of a COS Analysis Following extraction of the above data, the following analysis will be performed: -Assessment of the number and proportion of NIHR HTA applications referencing the COMET database or a COS published in the COMET database -Assessment of the number and proportion of NIHR HTA applications using a COS, if one exists, in their research These assessments will be used to draw conclusions about the potential impact on the use of COS of a research funder's recommendation about their use.
Thermic-2 was managed by the clinical trials unit with allocation determined by secure computerised system. Data capture: Data capture processes also changed between the two phases. A clinical fellow collected data on Excel spreadsheets in Thermic 1, whereas data were collected by research nurses in Thermic-2 and then entered into a purpose-designed database. Statistical analysis: Data from the two trials were pooled in one overall analysis adjusted for study phase. Interaction terms were added to the models to examine differences between trial phases.
Methods A logic model, a tool used to evaluate the implementation of a care programme  [4] , was developed with collaborating interventionists. We reviewed qualitative and quantitative datasets to identify components of the intervention logic model ("sorting"). A convergence coding matrix summarized similarities and differences between data sets for each of 17 logic model components, selecting examples to demonstrate how each had contributed to the intervention's success or failure ("convergence coding"). We applied a convergence coding scheme: "agreement"; "partial agreement"; "silence"; or, "dissonance". We quantified the level of agreement between data sets ("convergence assessment") And highlighted their different contributions to the research question ("completeness comparison"). We shared the triangulated results with team members and other selected stakeholders at a face-to-face meeting, for feedback, allowing points of disagreement to be discussed and changes in interpretation incorporated.
Method Five Clinical Research Networks (CRNs) invited GP practices close to trial's participating Secondary Care Sites (SCS) to join as Participant Identification Centres (PICs) on our behalf. GPs who agreed to participate as PICs screened their databases to identify potentially eligible patients and wrote to these patients inviting them to participate in the trial by contacting the trial's central coordinating office (CCO). The CCO further checked eligibility and referred patients to their local SCS. After 6 months of original request, a brief survey to identify main reasons for non-participation was sent to all GPs who declined participation.
Results The level of support provided to both the CCO and GPs varied across 5 CRNs potentially impacting GP participation rate. Overall, only 5% GPs agreed to participate and only 23 of 800 (3%) subsequently responded to the survey. The main reasons identified for nonparticipation were lack of time/resources to carry out database search (61%) and/or review medical records to confirm eligibility (48%), request coming at a busy time (9%) e. g. Calendar or financial year-end, and insufficient funding (26%). Encouragingly, 26% of GPs that completed the survey indicated willingness to participate at the time of the survey.
Methods Qualitative focus groups were conducted with 18 study coordinators and site investigators at sites actively participating in the global multicenter HARMONY Outcomes trial, an ongoing randomized controlled study to evaluate the effect of albiglutide on cardiovascular events in patients with Type 2 diabetes (Clinicaltrials. gov ID: NCT02465515). Interviews were conducted by a professional moderator using a semistructured, open-ended topic guide and were analyzed to identify common cross-site themes. Focus group participants represented research sites in the United States (n = 14), the United Kingdom (n = 2), Canada (n = 1), and Denmark (n = 1), with the majority based in multi-physician or hospital-based practices.
Conclusions Based on this review for most publicly funded trials the recruitment rate is likely to be between 1 and 2 participants per centre per week (4 to 10 a month). There is considerable variation in the consent, recruitment and retention rates in publicly funded RCTs. In practice, recruitment rates will vary, depending on whether the target population is acute, where opportunistic recruitment will target incident cases, or chronic, where database recruitment can effectively target prevalent cases. It will also vary according to whether the intervention is therapeutic or preventive and the base incidence and prevalence rate of the condition. Investigators should bear this in mind at the planning stage of their study and not be overly optimistic about their recruitment projections.
Analysis challenges The first challenge encountered was missing data; data were missing for between 10% and 45% of patients for some of the key data items. Secondly, in order to ensure the calculated risk score provides the best summary of the relationship between the non-modifiable factors and survival, it was important to ensure the most suitable functional form of each continuous covariate was used. Finally, the modelling required a survival analysis framework, which included ensuring that the appropriate model assumptions were met. To overcome these challenges multiple imputation using chained equations ("Ice" Command in Stata v14. 0) was used to impute the missing data items. Subsequently multivariable fractional polynomial models were fitted on the imputed dataset ("Mfpmi" Command), within a Cox model ("Stcox" Command).
The databases tested for citations were Medline®, Excerpta Medica Database (Embase), and Cumulative Index to Nursing and Allied Health Literature (CINAHL®). For each database, search terms were adapted according to the search capabilities of that database. To be included in the review, studies or trial protocols had to have been published in the last 10 years and report on any primary care intervention undertaken in a general practice setting in the context of a complex effectiveness trial. This included full trials, feasibility studies and/or pilot RCTs. Studies had to state in title or abstract that they had included assessment or reported on implementation fidelity. The initial searches resulted in 6253 citations across the three databases. Electronic abstract screening by two reviewers is underway.
Methods The following databases were searched: MEDLINE via PubMed, EMBASE, the Cochrane Central Register of Controlled Trials (CEN-TRAL), the Transplant Library and the National Institute of Health Clinical Trials database. Reference lists were also hand searched for other relevant trials/reviews. Research focusing on Kidney or Liver transplantation was reviewed. To be included in the review, the research results needed to specify the status of and/or analytical assessment of discard rates. Extracted information from studies meeting the inclusion criteria included: author, year, title, organisation, country, study type, organ type, donor type, categorisation of reference to discarded organs within research and outcomes reported. The quality of the studies included was assessed using the Cochrane Risk of Bias Tool; for assessing the quality of reviews, the ROBIS bias assessment tool was applied.
P103 Handling missing or incomplete data in a Bayesian network meta-analysis framework Danila Azzolina  In the first case, information about Delta variation are randomly removed, from full database, leaving only baseline end follow up data, then variability of mean change is imputed using the correlation method. In the other case, also information at baseline and follow up are removed, then Delta variability isimputed with maximum standard deviation method. On simulated dataset, NMA, with random effect and Uniform (0,5) prior on heterogeneity parameter, has been performed (MCMC method, 200000 iterations, 4 chains). To investigate robustness of NMA, under several scenarios and different imputation methods, the bias of rank probabilities estimates has been computed in order to check models performance in ranking treatments.
For each scenario, the mean, bias and the standard deviation of the first rank probability, for full and imputed databases, have been computed. The results show that the bias is very small for every scenario, then ranking provided by models is robust with respect to different imputation methods. The method is more robust to imputation in a low heterogeneity framework, especially if considered trials are conducted on similar population. Small bias is observed also for heterogeneity values similar to expectation of NMA heterogeneity prior, indicating more robustness if a priori knowledge is well specified.
Background The number of stroke rehabilitation trials reported is rapidly increasing. Efficient trial design contributing to advances in rehabilitation should be informed by completed trials in the field. More than 50,000 people in the UK each year acquire aphasia: a stroke related language impairment affecting the ability to speak, understand speech, read and write with significant consequences for quality of life. Existing Cochrane systematic review evidence indicates that speech and language therapy (SLT) benefits language recovery in people with aphasia, however, the specific patient and intervention factors which predict optimal recovery and rehabilitation are unclear. By using a wider dataset with individual patient data (IPD) analysis we are enhancing the evidence synthesis process with the aim of addressing these evidence gaps.
Methods We invited contributions of primary datasets from members of the Collaboration of Aphasia Trialists (cats). We also conducted a systematic search of existing published research to identify a comprehensive set of potentially existing aphasia research datasets which met our inclusion criteria. Research datasets were required to include a minimum of 10 people, a measure of aphasia severity as a consequence of stroke and information on time since stroke. We invited researchers from these studies to contribute data and to create a unique multilingual, international, interdisciplinary resource in this clinical field.
Results Following a systematic search of the literature, we screened 5276 titles (including 2346 abstracts and 1152 full texts), from which we identified 874 eligible studies. We have received 76 study datasets contributing IPD from 4597 people with aphasia (56 through the systematic search and 20 via cats). These data have been contributed from 23 countries and we have identified a further 2400 IPD in the public domain. The substantive challenge is our planned IPD metaanalysis to examine recovery, predictors of recovery and effectiveness of intervention approaches. Our statistical analysis plan states that a one-stage approach will be conducted for the primary analyses, although a two-stage approach will also be explored. Network meta-analyses and meta-regression (some of which includes subgroup analyses) are also planned.
We will discuss the methodological challenges, particularly which arise when there are non-standardized data, some non-randomized data, a large number of outcome measurements and some degree of sparse data. Conclusions RELEASE is the largest systematically developed, evidence synthesis study in the field of aphasia, and is more complex than most IPD trial meta-analyses. Our research will not only provide important evidence relating to the recovery of people with aphasia, but will also be an exemplar to researchers who plan to create databases to analyse complex individual patient data.
Methods Updates were made to the database design to enhance the user experience and additional reports were added. Emails were then changed from manual to automated to speed up the process of receipt acknowledgement and sending SAEs for medical review. A thorough training session was provided to TMs on the rota, delivered by the Senior Trial Managers and the database developer. All trials now have trial-specific SAE handling instructions stored centrally for easy access; these instructions give step-by-step guidance on how to handle an SAE for a particular trial. An oversight process was then put in place which involves, at a trial level, the specific-Trial Manager and, at a Unit-level, the team of Senior Trial Managers and QA Manager. A range of easy-to-use checklists were produced to ensure consistent regular oversight, undertaken on a rota basis.
Conclusion Implementing the database and rota system in 2013 was a step in the right direction. However, streamlining the process and implementing more robust oversight ensures that SAEs are handled appropriately and efficiently to ensure regulatory timelines are met. Including TMs in reviewing and updating the tracker database and relevant documentation has been essential to ensure buy-in from users. Clear communication and training whilst updating the process has also been crucial. TAPUR's pragmatic approach incorporates the clinician and patient perspective into the design. For example, enrollment is expanded through use of broader eligibility criteria, including participants with performance status (PS) 0-2, prior malignancies or HIV, and previously treated, but stable brain metastases.
Methods We identified eight non-drug trials from the Pragmatic Clinical Trials Unit (PCTU) administrative database for inclusion. These were selected because their trial documents were available to researchers electronically. Trial protocols, standard operating procedures (SOP) for adverse event reporting and the trial reports (where available) were reviewed. Data were extracted on 1) study design, population and intervention characteristics, 2) stated study objectives and outcomes, 3) data recording, managing and reporting process regards to adverse outcomes and 4) the use of data monitoring committees.
Background The establishment of databases to store information and results about a clinical trial, registered under an index number, has allowed for the identification and tracking of clinical trials. Consequently, the download and analysis of clinical trial information, whether published or unpublished, is possible and has allowed methodologists to take a closer look at characteristics and trends of clinical trials. While many trial databases exist, this report will focus on the clinicaltrials. gov registry. Clinicaltrials. gov became operational in 2000 and is mandated by the United States Government. Objective Using a case example, we will describe some of the challenges encountered when identifying and analyzing trials registered in clinicaltrials. gov.
Results In attempts to characterize NIH-funded clinical trials, we encountered: (1) Difficulty importing files into the statistical programs due to formatting issues (e. g. , presence of special characters, hard returns, commas, xml text); (2) Misclassification of trials due to misinterpretation of registration fields (e. g. , "funder" vs. "collaborator"; "interventional" vs. "observational"); (3) Duplicate counts of same trial as a result "obsolete" NCT identification numbers; (4) Misclassification of trials as to funding source (e. g. , trials that were funded by NIH were not listed as being NIH-funded in registry);  (5)  Missing registration fields (e. g. Number of sites, number of publications). While some of the limitations listed are dependent on how registrants interpret and enter information, there are steps that can be taken to improve registration.
One such step would involve improving the editing process to keep files "clean. " Providing a test environment for developers to explore data entry options and allowing for the creation of scripts to extract specific data from the website would also be useful. Second, clarifying definitions of registration fields and including examples would help avoid misinterpretation. Furthermore, refining the "other" Categories and using drop-down menus and checkboxes to avoid free-text would reduce incorrect entries and inconsistencies. Third, allowing "obsolete" NCT ids to be filtered out and providing reasons for why the ids were listed as obsolete would avoid double-counting trials. Finally, continuing to expand and update the registry will be important as more trials are registered in coming years, with an ultimate goal of having a worldwide registry for all trials.
Results Data relating to 82 hospital admissions were obtained for 74 patients at 5/15 sites. The remaining hospitals (10/15) were still in the process of setting up their PLICS systems and could only provide routine patient admissions or legacy finance database outputs in time for the study. The diabetes-related inpatient stays (N = 67/82 episodes) were comprised of the codes PA67Z (admission related to diabetic ketoacidosis) (12/67) and PA68Z (admission related to diabetes mellitus, without ketoacidosis or coma) (55/67). Mean costs (95% confidence intervals) for the diabetes-related codes were: (i) bed days: £662 (£587, £741); (ii) PBR: £1252 (£1230, £1278); and (iii) PLICS: £1839 (£1339, £2425). Disaggregated PLICS costs comprised medical/ specialist nursing staff (47%), wards/overheads (30%), critical care (8%), other clinical supply and services (6%), pharmacy/drug costs (5%), therapies (1%) and pathology (1%) with the remainder comprising blood supplies, imaging, operating theatre and other diagnostic tests.
The TEMPER management system To meet the specified requirements, we developed a data management system to allow: 1) the extraction of tailored trigger data from the participating trials databases; these had been developed in a third-party Clinical Data Management System; 2) the ranking and selection of triggered sites based on the extracted trigger data; 3) the pairing of the chosen triggered sites with control sites based on similarity criteria; 4) the collection and management of findings data gathered in the monitoring visits to the triggered and control sites. The system was developed in Visual Basic. NET with a SQL Server database, which served as the main study database for TEMPER and was used for analysis.
Trigger data For each participating trial, a set of specific triggers was defined by the trial team; each consisted of a narrative explaining the conditions under which it fires. An example of a trigger narrative is: 'More than 0. 5% of the values in the open forms are missing or queried based on total number of fields to be entered'. The majority of trigger narratives were implemented as automatic triggers in the system. When data was not available in the trial database to implement an automatic trigger, for example data was from external sources or based on subjective interpretation, manual triggers were instead created in the system allowing users to set their status to fire as necessary. The TEMPER system allowed the trigger data to be summarised following extraction, ranked by a trigger score based on the triggers fired, and presented to the trial teams to inform the selection of triggered sites to visit.
During the process of defining event classes, OWL tools can be used to check for errors. For example, it is possible to specify that two event classes should have no events in common, or that a group of event classes should be equivalent to some other event class. In order to use OWL event class definitions during the production of analyses and reports, some short 'glue' programs are required. Events (and their properties) that occur during the trial are converted into an OWL document and fed into an OWL reasoning tool. The output from this is a list of which event classes contain which other event classes. This is converted into a flat tabular form that can be stored in a database and easily queried to find out which events are in which classes. Pros This approach uses well-supported public domain software, so little in-house coding is required.
Methods We searched the Medline, Embase and Cochrane Library databases using keywords that identify randomised trials, mediation analysis and primary healthcare settings. Our eligibility criteria were papers published between 2004 and 2014 which described a mediation analysis of data from a randomised trial conducted in a primary healthcare setting. Abstracts were screened for eligibility, where eligibility was unclear from the abstract we performed full text screening. Data extraction related to the study design features and type of mediation analysis and assumptions was carried out independently by two authors. Any disagreement was resolved with a third reviewer.
Methods Using data from the MRC/BHF Heart Protection Study (HPS), the influence of demographic factors was explored at each stage of the trial recruitment process (invitation, screening, pre-randomisation run-in, and randomisation). The HPS dataset was chosen as patients had been identified as potentially eligible from their hospital records and then were sent a postal invitation to attend a screening visit. This method of invitation allows large numbers of patients to be invited, and provides a true, unselected denominator for the recruitment effort.
Aims To identify and synthesise knowledge about barriers and facilitators to recruitment to palliative care RCTs to develop recommendations to increase recruitment. Methods A systematic review with narrative synthesis. Social marketing theory provided a theoretical framework for the review. Medline, Cinahl, pscyinfo and Embase databases (from Jan 1990 to early October 2016) were searched. Papers included: interventional and qualitative studies addressing recruitment, primary palliative care RCTs or reports containing narrative observations about the barriers, facilitators or strategies to increase recruitment to palliative care RCTs. Themes within the literature were developed using thematic analysis. Results 3832 records were screened and data extraction was carried out on 48 papers. The key barriers: gatekeeping by professionals and family care givers, high refusal rates, the need for intensive resources and participants not meeting eligibility criteria.