dataesr/scientific-paragraphs-categorization huggingface dataset:
Background and Aims Patient adherence to treatment is a key determinant of outcome for healthcare interventions. Nonadherence likely influences therapeutic outcomes and, if unmeasured, introduces uncertainty into trial data. Whilst nonadherence has been well evidenced in settings such as drugs therapy, there is a paucity of information regarding patient adherence to rehabilitative or "removable" devices, such as splints and braces, whereby unscheduled removal may lead to irreversible deterioration of the injured structure. The aim of this study was to identify and summarise the methods reported in the literature for assessing patient adherence to removable devices to inform further research in this area and improve the quality of outcomes. Methods A systematic review was undertaken in seven electronic bibliographic databases including MEDLINE and CENTRAL, using terms relating to the device and assessment of adherence.
Methods We describe current electronic data sources that may be suitable for use in economic evaluations conducted alongside randomised controlled trials, including Hospital Episode Statistics (HES) and Secondary Users Service (SUS) datasets for secondary care in England; the Clinical Practice Research Datalink (CPRD), ResearchOne and inpractice software systems for primary care data; commissioning datasets and their potential use for research; as well as looking to future developments such as GP Connect and the need to link electronic datasets. We also describe the current status of instruments for measuring resource use via patient self-report, and discuss initiatives to improve the methodology. We assess the risks and benefits associated with each method, and compare the suitability of the two methodologies.
Discussion Patient self-report forms a pragmatic and cheap method of data collection that is largely under the control of the researcher. However, there are known issues with the validity of the data collected, loss to follow-up may be high, and questionnaires suffer substantially from missing data. Routinely collected electronic data may be more accurate, although the validity of the datasets is not commonly assessed for the purposes of health economic evaluation. The use of routine datasets is likely to be more practical than patient self-report if large numbers of patients are involved. However, access to datasets often incurs a significant cost and researchers are bound by the time for data approval and extraction by the data holders. Conclusions Owing to information governance and issues with timeliness of access associated with electronic datasets, self-reported methods may currently be the preferred option.
O87 Record linkage of trial datasets to hospital admission records: a trial manager's experience Suzanne Breeman, Jessica Wood, Graeme MacLennan, Jemma Hudson University of Aberdeen Correspondence: Suzanne Breeman Trials 2017, 18(Suppl 1):O87 Background Collection of outcome data within randomised controlled trials (RCTs) can be challenging, especially in trials with a lengthy follow-up phase. If the outcomes of interest are participant-reported, for example, qualityof-life, then participant contact is unavoidable. However, if the outcomes are clinical, for example, re-operation or hospitalisation then record linkage of trial datasets to hospital admission records (or other routine data-sets) may be an option and could potentially overcome some of the issues associated with participant retention.
Results The supplementation of participant-reported clinical outcomes with routine hospital admission data allowed us to better evaluate the key clinical question; approximately 20% of relevant hospital readmissions were obtained from routine admission data only. However the exercise also highlighted potential issues with the coding of routine admission data: for 5. 8% of index operations, the coding in the routine data did not match that recorded within the trial dataset. Discussion Inconsistencies between the participant-reported account and the record linkage report were detected. In an attempt to address this issue contact was made with the hospital site to extract information directly from the patients' notes. If no response was received, the participant's record of the event was included in the analyses in both trials.
Discussion Successful recruitment to the TIME study was mainly due to a combination of efficient study design with the use of IT to facilitate sending high numbers of study invitations at low cost. It is hoped that these methods can be used to streamline the recruitment process for future studies. Background Systematic reviews of clinical trials are an important means of synthesizing medical evidence. The validity of systematic reviews depends on the ability to fully capture the complete body of evidence through searches of many data sources. Nevertheless, examination of clinical trials registry databases is not routinely included in systematic reviews (Jones et al. , 2014). A possible explanation is that all databases have their own unique features and creating consistent search strategies across literature and registry databases may be challenging.
Materials and Methods The main idea is to use the title and abstract text fields of relevant articles, identified by a literature search, for training purposes of a machine learning algorithm. Next, the trained algorithm may be used to identify documents with a comparable word characterization in a registry database. A hypothetical research question concerning nursing research is used to demonstrate the proof-of-concept. The PubMed database was searched for articles published from 2006/01/ 01 to 2016/11/01, tagged with Clinical trial Publication Type and appearing in journals allocated to the Nursing subject area according to the Journal Citation Reports®. To provide a training set with "positives" and "negatives", we selected also a set of clinical trials run in other research fields. The following text pre-processing procedures were applied to the title and abstract fields of the retrieved records in the following order: conversion to lowercase, removing numbers, removing punctuation, removing stop-words, stemming words, stripping white space, and building a sequence of two adjacent words from the text (bigrams).
Methods Response and progression in MM were defined according to the International Myeloma Working Group (IMWG) Criteria. Myeloma XI collected local laboratory results alongside local response assessments, and for consenting patients -equivalent data from an experienced central laboratory. All data was stored on bespoke trial databases which could be easily imported into statistical software packages. We developed a suite of SAS programs which apply the IMWG criteria to reported laboratory values and verified local and central response and progression assessments, and validated local and central assessments against each other. If assessments were concordant, these were considered to be validated and required no further review. If results were discordant, the patient file was selected for review by a committee of clinicians.
Methods In a collaborative effort between study and surveillance staff, aggregate clinic outcomes were defined using variables available in surveillance data and aligned with the financial incentive intervention. Outcomes were centrally programmed by surveillance experts for evaluation using local surveillance databases. Pre-trial data on trial outcomes were used to conduct a restricted randomization of the 38 trial clinics, with the goal of achieving balance in pre-trial viral load suppression and number of patients per clinic. During the trial, triangulation of data from the clinics, surveillance and the financial incentive delivery was used to assess data completeness. Sensitivity analysis and multiple imputation were subsequently used to evaluate 1) randomization balance based on incomplete pre-trial data in the restricted randomization; 2) the impact of incompleteness in baseline data on efficacy evaluation and 3) the sensitivity of the trial results to sites with missing data during the primary evaluation period.
Background Since 2008 U. S. federal law requires the submission of summary results to, and public posting on, ClinicalTrials. gov for certain clinical trials of FDA-regulated drugs, biologics and devices. We aimed to determine ways in which reporting to ClinicalTrials. gov has expanded the public availability and use of clinical trial information. Specifically, we assessed the extent to which ClinicalTrials. gov has improved the reporting of "full sets" of trials for specific drug products (aim 1). We also examined how researchers have used ClinicalTrials. gov data (aim 2).
Methods For aim 1, we examined ClinicalTrials. gov records for phase 2-4 clinical trials of drugs or biologicals with at least one U. S. study location, completed or terminated between 2007-2009, and funded by industry. We grouped trials into a "drug trial family" when the drug, condition, and sponsor appeared to be the same across trials. We limited our analysis to a convenience sample of the first 96 drug trial families identified. For each trial, we assessed the presence of results posted on ClinicalTrials. gov and publications cited in PubMed as of June 2015. For aim 2, we searched PubMed to identify publications in English between 2010-2015 that conducted original research using data retrieved from the ClinicalTrials. gov registry and/or results database. Authors manually reviewed eligible publications to categorize how ClinicalTrials.
Results For aim 1, our sample of 96 drug trial families included 329 trials, studying 86 drugs for 78 conditions by 45 companies. Of the 329 trials, 109 (33%) had results posted on ClinicalTrials. gov only, 42 (13%) in PubMed only, 81 (25%) in both, and 97 (29%) in neither. Of the 96 drug trial families, 45 families had results disclosed in at least one source for all trials. Of these 45 families, 15 families had results disclosed only in ClinicalTrials. gov. For 18 drug trial families with a total of 48 trials, no results were available from any source. For aim 2, we identified 404 research articles and 1,588 systematic reviews published between 2010-2015 that used data from the Clini-calTrials. gov registry, results database, or both. We categorized each research article into: characterization of clinical research for specific conditions (48%); research on ethics, adverse event reporting, and data mining (15%); quality of registered data and consistency with reporting policies (10%); characterization of the overall clinical research landscape (10%); evaluating publication bias or selective reporting (9%); and assessing specific research-related methods and issues (7%).
Conclusion We found that results for 33% of sampled clinical trials were available only on ClinicalTrials. gov, suggesting the database may be a useful resource to identify clinical trial results that would not otherwise be publically available for specific drug products. Although there were still gaps in the public availability of trial results, the existence of the trials is publicly known because of the registry entries. ClinicalTrials. gov data is increasingly being used to assess a broad range of research questions, many of which would have been difficult or impossible to address using published literature alone.
Method: The literature databases PubMED, Web of Science and Wiley Online Library were searched to identify published first-in-human trials of small molecule oncology candidates. Search terms used were:  [1]  "first-in-man" [title] OR  [2]  "first-in-human" [title] OR  [3]  "oncology OR cancer". Further searches were completed which replaced terms  [1]  and  [2]  with  [4]  "dose escalation". A total of 69 trial publications applicable to the aim of this review were identified. Each publication was reviewed and relevant data extracted which included the type of dose escalation method and individual dose levels used. To enable comparative analysis of 3 + 3 and "accelerated trial design" (ATD) escalation methods (the most popular methods with trials of suitable number for comparison), dose level data was translated to "Unique Dose Levels" (UDLs).
; 2 Bangor University Correspondence: Graham Powell Trials 2017, 18(Suppl 1):O3 There are a number of administrative datasets that routinely record information on individuals in the UK. Such routine or 'Big Data' sources record data for a specified primary purpose and are regulated for security, confidentiality and disclosure by The Data Protection Act 1998 and Freedom of Information Act 2000. Access for secondary uses such as clinical research is permitted when health and social care benefit can be demonstrated. Routine sources include clinical datasets such as Hospital Episode Statistics (HES) and the Clinical Practice Research Datalink (CPRD) and non-clinical datasets such as data recorded by the Department of Work and Pensions (DWP), HM Revenue and Customs (HMRC) and The Driving and Vehicle Licensing Authority (DVLA).
Methods to optimise this have not been previously explored. The aim of this study is to summarise methods to optimise recruitment into trials in UHC settings to inform future research. MethodsThe ORRCA (Online Resource for Recruitment Research in Clinical Trials; www. orrca. org. uk) database includes studies of all designs, systematically extracted from the literature, reporting on recruitment into RCTs and non-randomised clinical studies. Included in this review were any studies within ORRCA that reported on recruitment to RCTs in an UHC setting in patients >18 years. Abstracts, editorials, reports of mental health RCTs and studies of recruitment to non-RCTs were excluded. UHC was defined as the care received during an unpredictable admission to hospital at short notice because of clinical need.
SSCI) within the ISI Web of Science and ERIC in January 2015. Database specific search strategies were developed based on previous work by Treweek et al. 2010. Inclusion Criteria Studies reporting or evaluating strategies, interventions or methods used to recruit patients to randomised control trials, early phase trials, qualitative interviews, focus groups, surveys, biobanks and cohort studies. Case reports of recruitment challenges or successes and studies exploring reasons for patient participation or refusal are also included. Methods Articles were screened by title and abstract before a full text review by researchers from the Hub for Trials Methodology Research Recruitment Working Group (HTMR RWG) in the UK and the Health Research Board for Trials Methodology Research Network (HRB-TRMN) in Ireland.
MethodsThree Chinese electronic biomedical literature databases (sinomed, CNKI and Wanfang) were searched to identify randomized clinical trials (RCTs) of TCM for stable angina pectoris published in 2010. RCTs evaluated the effectiveness of TCM for stable angina pectoris were considered to be eligible. There was no limitation to the type of TCM intervention and control. All reported outcomes and related information of included trials were extracted. Two authors screened literatures and extracted information dependently. ResultsFinally ninety-four RCTs (with 9111 subjects) were included for analyses. There were totally 79 outcome measures were reported in all included RCTs. The ten mostly reported outcomes were efficacy rate of ECG, efficacy rate of angina pectoris, efficacy rate of TCM syndrome, fasting lipid profile, withdrawal rate of nitroglycerin, total scores of TCM syndrome, rate of clinical efficacy, nitroglycerin consumption, hemodynamic indexes, endothelin, efficacy rate of individual symptoms of TCM, frequency of angina attack.
Goals of the overall project include reducing study start-up time; increasing the efficiency and effectiveness of clinical research studies and treatment; and increasing data quality and data sharing across studies. BackgroundCDEs were developed for 18 neurological disease areas prior to these five newly developed areas. There are over 11,000 CDEs in the NINDS library and over 600 validated instruments, Case Report Forms (CRFs), and study recommendations. The CDE initiatives strive to identify CDEs, template study forms, data dictionaries and guidelines to assist investigators who are initiating and conducting related clinical studies. Use of standardized CDEs is critical to ensure that data housed within a database are of better quality; improve data sharing and meta-analyses; and help educate new clinical investigators.
Methods We included all trials identified by the Cochrane Stroke Group's trial register. This includes trials identified from 35 electronic databases (e. g. Medline, CINAHL, EMBASE); numerous clinical trial registers; and hand-searching other resources. We included publications between 2005-2015, grey literature, and had no language restrictions. Records that reported RCTs of stroke rehabilitation non-pharmacological interventions with patient populations were included. We extracted recruitment efficiency data (i) Rate: numbers randomised as a percentage of those screened for eligibility (ii) Speed: average monthly recruitment numbers across sites (iii) overall Dropout rates. Data on recruitment sites, recruiters, setting, funding support, ethical review, intervention type, targeted impairment, control comparison, and country of recruitment were extracted by two independent reviewers.
Acknowledgments This work was conducted as part of the inspire (Innovative methodology for small populations re-search) project funded by the  European Union 's  Seventh Framework Programme  for research, technological development and demonstration under grant agreement number  FP HEALTH 2013-602144 , but does not necessarily represent the view of all inspire partners. 1  University of Minnesota, School of Public Health, Division of Biostatistics ; 2  George Washington University School of Medicine ; 3 Kirby Institute, University of New South Wales; 4  Hospital General Universitario Gregorio Marañón ,  Instituto de Investigación Sanitaria Gregorio Marañón, Departamento de Microbiología I/Inmunología ,  Facultad de Medicina ,  Universidad Complutense de Madrid ; 5  National Institute of Allergy and Infectious Disease, Biostatistics Research Branch ; 6  UT Southwestern Medical Center, Department of Internal Medicine ; 7  CRG, Infection and Population Health, UCL  and  MRC CTU  at  UCL, University College London Correspondence :  Ross Peterson  Trials 2017, 18(Suppl 1):P452 1 The University of Liverpool; 2  Clinical Trials Research Centre (CTRC), North West Hub for Trials Methodology Research, University of Liverpool ; 3 Patient and Public Involvement partner; 4  Department of Psychological Sciences, North West Hub for Trials Methodology Research, University of Liverpool; 5 University College London and National Institute for Health Research (NIHR ), INVOLVE; 6  Leeds Institute of Clinical Trials Research (LICTR) ,  University of Leeds ; 7  Health Care Research Wales , Public Involvement and Engagement, Castlebridge 4; 8  NIHR Biomedical Research Centre  and  Nuffield Department  of  Primary Care Health Sciences, University of Oxford; 9 Centre for Primary Care, University of Manchester; 10 Centre for Public Health, Queen's University of Belfast Correspondence : Kerry Woolfall Trials 2017, 18(Suppl 1):O53 1  Leiden University Medical Center ; 2 University Medical Center Utrecht; 3  Radboud University Medical Center ; 4  University College London Hospital ; 5 MRC Clinical Trials Unit at  UCL, Institute of Clinical Trials and Methodology, UCL Correspondence : Carlo Lancia Trials 2017, 18(Suppl 1):O68 Acknowledgments JT acknowledges funding from the  ConDuCT-II Hub for Trials Methodology Research.
Fifty trials are simulated in full databases by including baseline, follow-up and Delta variation in- formation. Baseline data are obtained by sampling from bounded 0- 100 normal distributions (X ~N(41. 8,21. 5)) (Cannon, 2000), to mimick the support of WOMAC score. Delta variation data are simulated from normal distributions with parameters provided by a review about 6 FANS treatments. Follow up variability data are provided from gener- ated Delta and baseline variability measures setting hypotheses on 1 University of Padua; 2 University of Turin Correspondence: Danila Azzolina Trials 2017, 18(Suppl 1):P103A Bayesian NMA model is often used to estimate the effect of each intervention compared to others synthesizing results using rank probabilities. In several cases, a NMA is associated to a loss of information due to pre-post correlation and considering, in each scenario, a sequence from 0.
3 to 0. 95 by 0. 05. Sample size are obtained by sampling from an uniform 50-100 distribution. Between trial heterogeneity has been included as a variability measure by following, for each simulation setting, a sequence from 0. 1 to 5 by 0. 1. Each scenario provides different combinations of heterogeneity between trials and pre-post correlation creating 700 scenarios. For each scenario 2 imputed databases are generated.
Relevant full text articles were reviewed to identify the type of adaptive methodology applied, if the publication explicitly stated the use of adaptive designs and whether adaptive design methodology was applied prospectively or concurrently. Results The databases produced a total of 14544 phase II, phase III, phase II/ III cancer trial related articles that were published in 2015. Of which 99/2127 (5%) for the PubMed database, 519/9573 (5%) for the Embase database and 116/2844 (4%) for the Ovid database articles included the search terms related to adaptive design methodology. After the removal of duplicates, 464 articles were remaining. Of the 464 articles, 92 (20%) were full text trial related publications, 261 (56%) were abstracts, 32 (7%) were methodology or review papers and 79 (17%) were not related to the search criteria.
The adaptive design methodology used in over half of the trials (48/92) was applied by performing interim analyses due to safety, efficacy or futility, 21 out of 92 trials incorporated dose escalation methods and 14 out of 92 implemented a two-stage design. The remaining 9 trials applied the following methods: Bayesian adap- tive design (3/92), group sequential design (3/92) change in pri- mary endpoint (1/92), seamless phase II to phase III (1/92), multiple adaptive (1/92 The aim of this litera- ture review is to understand the current application of adaptive design methodology in oncology trials, and to ascertain how this methodology is reported. MethodsA literature search of PubMed, Embase and Ovid databases for full text publications of phase II, phase III or phase II/III cancer trials using adaptive design methodology during 2015 was conducted.
The aim of the 'Lessons Learnt' database developed at Keele CTU is to capture and learn from positive and negative events or pro- cesses which occur during the life of a research study, and to communi- cate these to others. The overall objective is to make improvements to, and streamline the processes of setting up and running a research study. Methods A 'Lessons Learnt' database specification was written following the generation of ideas by a group of experienced Trial Managers and Data Managers. The database was built using Access, and iterative piloting was undertaken until the group were satisfied with the data- base functionality. Within the database, lessons are grouped by study and are recorded by: study registration details, stage of study (e. g. Planning and de- sign), process area (e.