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Who speaks and with what authority, who is believed, what evidence is introduced, and how it is presented are all informed not only by the substantive law and the rules of evidence but also by the rituals of the trial. It is from this legal process as a whole that a judge or jury determines the (legal) 'truth' about a woman's allegation of rape. A sexual assault complainant's capacity to be believed in court, to share in the production of meaning about an incidence of what she alleges was unwanted sexual contact, requires her to play a part in certain rituals of the trial. Many of these rituals are hierarchical, requiring complainants to perform subordinate roles that mirror the gender-, race-, and socio-economic status-based societal hierarchies in which the problem of sexual violence is rooted. Relying on the work of Robert Cover and interdisciplinary work on ritual for its conceptual framework, this article pursues two objectives. First, it attempts to depict, through the use of trial transcripts, the brutality of the process faced by sexual assault complainants. Second, it exposes some of the institutionalized practices, as manifested through courtroom rituals, that contribute to the inhospitable conditions faced by those who participate in the criminal justice response to sexualized violence.

BackgroundThe UK MS Register (UKMSR) developed an electronic version the Symbol Digit Modalities Test (SDMT) called the Cognitive Reaction (CoRe) test that has been validated within clinics on tablet computers. We developed a new version of the CoRe application (V2) for use on personal devices but also incorporates a range of symbols.AimTo establish the responsiveness to different symbol sets using the CoRe app.MethodsCoRe was adapted for use on Apple devices. Symbols sets were added, including: classic, emoji and pictographs. We recruited MS Register participants, with a link made available to the Apple testflight application.Results191 pwMS downloaded CoRe V2, 185 were linked to UKMSR data: 116 Relapsing, 61 Progressive, 8 unspecified pwMS. 131 used an iPhone and 60 a tablet. 644 tests were completed, with a mean per pwMS 3.05±2.5 (mean±SD). 264 completed the ‘classic’ symbols: answered 42.2±12.8, correct 41.5±13.6; 220 completed ‘emojis’, answered 54.6±16.07, correct 53.8±16.5; 160 completed ‘pictographs’, answered 44.6±14.2, correct 43.6±14.5. Linear regression with correct score as the dependent variable found that Emoji scores were increased by 12.5 [10, 15] and scores were decreased -0.5 per year [-0.6, -0.4].ConclusionPwMS performed better at a younger age and when using the emoji symbols compared to the classic and pictograph symbols. The basis of this difference requires further investigation.

BackgroundThe UK MS Register (UKMSR) collects multiple sclerosis (MS) clinical data from UK NHS specialist treatment centres. Relevant clinical data for research is difficult to access and time consuming to obtain- clinical letters remain the canonical record for patient interaction. We obtained outpatient letters from 11 NHS sites and used natural language processing (NLP) techniques to harvest data.AimCapture minimally relevant clinical dataset from outpatient letters.MethodWe defined and implemented a ruleset using General Architecture for Text Engineering (GATE). Seeking forename, surname, DOB, gender, NHSNumber, clinic-date, postcode, MSType and EDSS. In a validation task, 100 randomly selected letters were manually reviewed by domain experts and results were compared with those of the algorithm for accuracy.Results2436 letters from 771 individuals were analysed. F-measures were: forename (99.0%), surname (99.0%), DOB (100.0%), gender (94.7%), NHSNumber (100.0%), clinic-date (94.4%), postcode (99.5%), MSType (95.7%), EDSS (97.4). Overall, 115 new EDSS scores were obtained.ConclusionNLP can be used to accurately extract information from free text in outpatient letters. This process can be built into a routine ingestion pipeline so that information can regularly be added to routinely collected clinical data, enhancing the data used for research at the UKMSR.

Cognitive impairments in Multiple Sclerosis (MS) are prevalent and disabling yet often unaddressed. Here, we optimised automated online assessment technology for people with MS and used it to characterise their cognitive deficits in greater detail and at a larger population scale than previously possible. The study involved 4526 UK MS Register members over three stages. Stage 1 evaluated 22 online cognitive tasks and established their feasibility. Based on MS discriminability a 12-task battery was selected. Stage 2 validated the resulting battery at scale, while Stage 3 compared it to a standard neuropsychological assessment. Clustering analysis identified a prevalent MS subtype exhibiting significant cognitive deficits with minimal motor impairment. Disability in this group is currently unrecognised and untreated. These findings underscore the importance of cognitive assessment in MS, the feasibility of integrating online tools into patient registries, and the potential of such large-scale data to derive insights into symptom heterogeneity. Online cognitive testing combined with the reach of a digital patient registry revealed a subtype of people with Multiple Sclerosis with significant cognitive impairment but no motor deficits. This hidden disability is often underestimated and left untreated.

IntroductionThe UK MS Pregnancy Register gathers pregnancy data in women with multiple sclerosis (MS) to address research gaps. We present data from >100 participants recruited since initiation in 2021.MethodsWomen with MS enter data via a dedicated online portal, completing questionnaires at 2 timepoints during pregnancy, and 3- and 12 months post-partum.ResultsOf 131 participants, most were White ethnicity (91%), and had relapsing-remitting MS (94%). 101 (89%) had ever taken DMT. Mean age at diagnosis and registration were 28.6 and 33.1 years respec- tively. Most registered in the first trimester of pregnancy (47.3%, n=62). Median EDSS was 2.5. 18 relapses were recorded in 15 patients; most relapses occurred in late pregnancy and postpartum. 78% discussed pregnancy in advance with their MS team. 86 pregnancies were exposed to DMT (62.3%), most commonly natalizumab (34.9%, n=30), glatiramer acetate (20.9%, n=18), and dimethyl fumarate (12.8%, n=11). 120 participants (91.6%) took supplements during pregnancy. Delivery gestation varied from 38 to 41 weeks, with the majority having delivered at 40 weeks. Postnatally, 58.3% of participants scored >10 on the Edinburgh PND scale.ConclusionSuch data demonstrates feasibility of large-scale, patient-orientated data gathering. Further work is required to improve postpartum data acquisition.

BackgroundTo improve trial efficiency (time and cost) the Optimal Clinical Trials Platform for Progressive Multiple Sclerosis (OCTOPUS) trial [ISRCTN 14048364] will test a number of treatments, to slow disability in Progressive MS (PMS) in a multi-arm multi-stage fashion: a MAMS Platform. Timely recruitment is important and the UK MS Register (UKMSR) has designed and is operating the registration of interest (ROI) portal. Aim: To examine the initial wave of potential participants for OCTOPUS.MethodWe built a dual-element recruitment platform comprising, UKMSR and non-UKMSR (similar, but without previously collected data). All registered were emailed to complete follow-up survey with specific eligibility criteria. Aggregated data were made available to the study team for further clinical assessment.ResultsRegistrants: 569 non-UKMSR-ROI, 1637 UKMSR.As of: 11/1/2023 follow up data were provided by: Non-UKMSR: 413;153(37%) meeting eligibility. 30(7%) currently ineligible. UKMSR: 860; 659(40%) meeting eligibility. 311(19%) currently ineligibleParticipants were ineligible for reasons including: age (25-70 inclusive allowable), no evidence of pro- gression, unable to tolerate MRI (required for stage 1), contraindicated medication. 812 participants are eligible with 82% having UKMSR patient-reported outcome data.ConclusionTrial recruitment can be challenging. UK MS Register eligibility screening methods will be valuable in ensuring the trial recruits participants quickly, ensuring efficiency and cost effectiveness.

BackgroundIdentification of multiple sclerosis (MS) cases in routine healthcare data repositories remains challenging. MS can have a protracted diagnostic process and is rarely identified as a primary reason for admission to the hospital. Difficulties in identification are compounded in systems that do not include insurance or payer information concerning drug treatments or non-notifiable disease.AimTo develop an algorithm to reliably identify MS cases within a national health data bank.MethodRetrospective analysis of the Secure Anonymised Information Linkage (SAIL) databank was used to identify MS cases using a novel algorithm. Sensitivity and specificity were tested using two existing independent MS datasets, one clinically validated and population-based and a second from a self-registered MS national registry.ResultsFrom 4 757 428 records, the algorithm identified 6194 living cases of MS within Wales on 31 December 2020 (prevalence 221.65 (95% CI 216.17 to 227.24) per 100 000). Case-finding sensitivity and specificity were 96.8% and 99.9% for the clinically validated population-based cohort and sensitivity was 96.7% for the self-declared registry population.DiscussionThe algorithm successfully identified MS cases within the SAIL databank with high sensitivity and specificity, verified by two independent populations and has important utility in large-scale epidemiological studies of MS.

The use of restrictive practices has significant adverse effects on the individual, care providers and organisations. This review will describe how, why, for whom, and in what circumstances approaches used by healthcare organisations work to prevent and reduce the use of restrictive practices on adults with learning disabilities. Evidence from the literature will be synthesised using a realist review approach - an interpretative, theory-driven approach to understand how complex healthcare approaches work in reducing the use of restrictive practices in these settings. In step 1, existing theories will be located to explore what approaches work by consulting with key topic experts, holding consultation workshops with healthcare professionals, academics, and experts by experience, and performing an informal search to help develop an initial programme theory. A systematic search will be performed in the second step in electronic databases. Further searches will be performed iteratively to test particular subcomponents of the initial programme theory, which will also include the use of the CLUSTER approach. Evidence judged as relevant and rigorous will be used to test the initial programme theory. In step three, data will be extracted and coded inductively and deductively. The final step will involve using a realist logic of analysis to refine the initial programme theory in light of evidence. This will then provide a basis to describe and explain what key approaches work, why, how and in what circumstances in preventing and reducing the use of restrictive practices in adults with learning disabilities in healthcare settings. Registration

Incorporating cognitive testing into routine clinical practice is a challenge in multiple sclerosis (MS), given the wide spectrum of both cognitive and physical impairments people can have and the time that testing requires. Shortened paper and verbal assessments predominate but still are not used routinely. Computer-based tests are becoming more widespread; however, changes in how a paper test is implemented can impact what exactly is being assessed in an individual. The Symbol Digit Modalities Test (SDMT) is one validated test that forms part of the cognitive batteries used in MS and has some computer-based versions. We developed a tablet-based SDMT variant that has the potential to be ultimately deployed to patients' own devices. This paper aims to develop, validate, and deploy a computer-based SDMT variant, the Cognition Reaction (CoRe) test, that can reliably replicate the characteristics of the paper-based SDMT. We carried out analysis using Pearson and intraclass correlations, as well as a Bland-Altman comparison, to examine consistency between the SDMT and CoRe tests and for test-retest reliability. The SDMT and CoRe tests were evaluated for sensitivity to disability levels and age. A novel metric in CoRe was found: question answering velocity could be calculated. This was evaluated in relation to disability levels and age for people with MS and compared with a group of healthy control volunteers. SDMT and CoRe test scores were highly correlated and consistent with 1-month retest values. Lower scores were seen in patients with higher age and some effect was seen with increasing disability. There was no learning effect evident. Question answering velocity demonstrated a small increase in speed over the 90-second duration of the test in people with MS and healthy controls. This study validates a computer-based alternative to the SDMT that can be used in clinics and beyond. It enables accurate recording of elements of cognition relevant in MS but offers additional metrics that may offer further value to clinicians and people with MS.