Suicide is a major public health issue and a leading cause of death among children and young people (CYP) worldwide. There is strong evidence linking adverse childhood experiences (ACEs) to an increased risk of suicidal behaviours in adults, but there is limited understanding regarding ACEs and suicidal crises in CYP. This study aims to examine the ACEs associated with CYP presenting at Emergency Departments for suicidal crises, and specifically the factors associated with repeat attendances. This is a case series study of CYP (aged 8–16) experiencing suicidal crisis who presented in a paediatric Emergency Department in England between March 2019 and March 2021 (n = 240). The dataset was subjected to conditional independence graphical analysis. Results revealed a significant association between suicidal crisis and several ACEs. Specifically, evidence of clusters of ACE variables suggests two distinct groups of CYP associated with experiencing a suicidal crisis: those experiencing “household risk” and those experiencing “parental risk”. Female sex, history of self-harm, mental health difficulties, and previous input from mental health services were also associated with repeat hospital attendances. Findings have implications for early identification of and intervention with children who may be at a heightened risk for ACEs and associated suicidal crises.

Objective: We develop and externally validate two models for use with radiological knee osteoarthritis. They consist of a diagnostic model for KOA and a prognostic model of time to onset of KOA. Model development and optimisation used data from the Osteoarthritis initiative (OAI) and external validation for both models was by application to data from the Multicenter Osteoarthritis Study (MOST). Materials and methods: The diagnostic model at first presentation comprises subjects in the OAI with and without KOA (n = 2006), modelling with multivariate logistic regression. The prognostic sample involves 5-year follow-up of subjects presenting without clinical KOA (n = 1155), with modelling with Cox regression. In both instances the models used training data sets of n = 1353 and 1002 subjects and optimisation used test data sets of n = 1354 and 1003. The external validation data sets for the diagnostic and prognostic models comprised n = 2006 and n = 1155 subjects respectively. Results: The classification performance of the diagnostic model on the test data has an AUC of 0.748 (0.721–0.774) and 0.670 (0.631–0.708) in external validation. The survival model has concordance scores for the OAI test set of 0.74 (0.7325–0.7439) and in external validation 0.72 (0.7190–0.7373). The survival approach stratified the population into two risk cohorts. The separation between the cohorts remains when the model is applied to the validation data. Discussion: The models produced are interpretable with app interfaces that implement nomograms. The apps may be used for stratification and for patient education over the impact of modifiable risk factors. The externally validated results, by application to data from a substantial prospective observational study, show the robustness of models for likelihood of presenting with KOA at an initial assessment based on risk factors identified by the OAI protocol and stratification of risk for developing KOA in the next five years. Conclusion: Modelling clinical KOA from OAI data validates well for the MOST data set. Both risk models identified key factors for differentiation of the target population from commonly available variables. With this analysis there is potential to improve clinical management of patients.

Background: Appropriate medication use is essential in ensuring optimal pharmacotherapeutic outcomes. It is mistakenly assumed that adults can swallow solid oral dosage forms (SODFs, e.g. tablets/capsules colloquially referred to as ‘pills’), without difficulty and that children cannot. KidzMed is a ‘pill swallowing’ training programme designed to teach effective SODF use in patients of all ages. It may be utilised by healthcare professionals to assist patients taking SODFs. E-learning was essential for training during COVID pandemic to reduce viral transmission. The aim of this study was to explore UK student pharmacists views of e-learning to support swallowing solid oral dosage forms. Methods: This study used pre- and post-intervention online surveys on Microsoft Forms to evaluate self-directed eLearning about pill swallowing on MPharm programmes at three UK Universities using a 13-item survey. A combination of five-point Likert Scales and free-text items were used. The eLearning was available via the virtual learning environment at the University and embedded within existing curriculum. Descriptive statistical analysis was used to explore responses. Results: In total, 113 of 340 (33%) students completed the survey. Seventy-eight percent (n = 65) reported the eLearning would enable them to teach adults and children to swallow SODFs successfully. Learners either agreed or strongly agreed that they felt comfortable to teach patients (95%, n = 62/113) and parents or carers (94%, n = 60) to swallow medications having completed the e-learning. Student pharmacists generally found eLearning as an acceptable way to reflect on their own experiences of ‘pill’ swallowing and how to support patients to swallow SODFs. Conclusion: The KidzMed eLearning was well received by student pharmacists. Further work is needed to explore whether skills translates into real life application in the clinical settings.

The high-stress nature of policing contributes to deterioration of officer health and wellbeing as well as high levels of absenteeism and attrition. Wearable technology (WT) has been identified as a potential tool that can help in improving officer health and wellbeing. This pilot study aimed to give initial insight into acceptability and engagement with WT amongst officers. The study also aimed to uncover any notable areas for exploration in future research within the domain of officer health and wellbeing. Two groups were observed, firearms officers and a mixed group of officers. Participants wore the WT for an extended period, completed a variety of health and wellbeing questionnaires and discussed their experience in focus groups. Firearms officers and mixed group officers displayed similar sleep efficiency, but firearms officers have worse sleep consistency and sleep performance. Firearms officers appear to have higher HRV and a slightly lower resting heart rate. Both groups display reasonable acceptance of the use of WT, speaking favorably during the focus groups of how monitoring the data had improved their quality of life in terms of their understanding of sleep, wellbeing and how they had consequently completed lifestyle modification. WT offers some promise in managing officer health and wellbeing; studies with larger sample sizes are needed to confirm this.

Being able to predict the activity of chemical compounds against a drug target (e.g. a protein) in the preliminary stages of drug development is critical. In drug discovery, this is known as Quantitative Structure Activity Relationships (QSARs). Datasets for QSARs are often ill-posed for traditional machine learning to provide meaningful insights (e.g. very high dimensionality). Here, we propose a multi-task learning (MTL) approach to enrich the original QSAR datasets with the hope of improving overall QSAR performance. The proposed approach, henceforth named MTL-AT, increases the size of the useable data by the use of an assistant task: a supplementary dataset formed by compounds automatically extracted from other possibly related tasks. The main novelty in our MTL-AT approach is the addition of control for data leakage. We tested MTL-AT in two drug discovery scenarios: 1) using 100 unrelated QSAR datasets, and 2) using 20 QSAR datasets that are related to the same protein family. Results were compared against equivalent single-task approach (STL). MTL-AT outperformed STL in 45 tasks of scenario 1, and in 12 tasks of scenario 2. The best overall method appears to be MTL-AT on both scenarios, with the small datasets yielded the best performance improvement from using multi-task learning. These results show that implementing multi-task learning with QSAR data has promise, but more investigation is required to test its applicability to certain features in datasets to make it suitable for widespread use in the drug discovery area. To the best of our knowledge, this is the first study that benchmarks the use of MTL on a large number of small datasets, which represents a more realistic scenario in drug development.

Objective: To compare diagnostic models for radiological KOA at KL2 + using sex-specific variables against a generic model with sex as an input. Data from the Osteoarthritis Initiative (OAI) was used for model development and optimisation. Materials and methods: Current models for diagnosis of knee osteoarthritis (KOA) at first presentation comprise subjects in the OAI dataset with and without KOA. We select subsets of the OAI data set for which additional sex-specific variables are available, resulting in male and female cohorts of size n = 1250 and n = 1442, respectively. Results: The classification performance of the previous diagnostic model on the test data has an area under the curve (AUC) of (95% CI 0.721–0.774) when only variables common to both sexes were entered for model selection and sex was a separate input. When tested separately on the male only and female cohort the test performance of the generic model gives baseline AUCs of (95% CI 0.689-0.770) and (95% CI 0.728-0.799) respectively. The sex-specific models for males and females yield AUCs of (95% CI 0.684-0.765) and (95% CI 0.731-0.803) respectively, Discussion: Fitting sex-specific models allows additional variables to be entered in the pool for model selection compared with a generic model with sex as a covariate. The focus of this study is whether the specificity of the additional data enhances their predictive power of logistic regression modelling for the diagnosis of incident radiological KOA in the OAI dataset, at first presentation. The performance of the generic and sex-specific models is comparable, since the confidence intervals for all of the models overlap. Nevertheless, some relevant variables after feature selection v are sex-specific, indicating that incidence of KOA at baseline presentation is associated with sex-specific attributes. Conclusion: This specialisation of the sex-specific models indicates potential differences in the aetiology leading to disease onset and may provide greater utility to both clinicians and subjects. For instance, the risk factors identified by the specialised models provide quantitative indicators that useful for early identification of females at higher risk of KOA, prompting them to take proactive measures to improve joint health at an earlier stage in life.