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The COVID-19 pandemic has changed forever the way we do certain things. Although the race for a cure and vaccine has taken centre stage, traditional face-to-face medical education has slowly metamorphosised in the background to a virtual world with innumerable webinars, virtual tutorials and lectures in the World Wide Web. Despite this seemingly ‘perfect’ solution, there remains a hidden cost. Educators are forced to learn new skills to engage students as well as manipulate the electronic platform. Impact on learning for students, both undergraduate and postgraduate from a lack of social interactions, remains unknown. In this article, the authors share their experiences from different specialities about the pros and cons of virtual learning and teaching. Suggestions and practical tips are offered to enhance the learning experience. More emphasis may need to be placed on the creation of learning communities rather than lecture-based curricula. Hybrid curricula or conferences may become the future norm. As we slowly move out of lockdown into a changed world and new ways of doing things, lessons learnt can be harnessed for future hybrid models that can combine the best of technology and physical teaching to reduce worldwide inequalities.

Purpose: In the Clinical Frailty Scale (CFS), level 6 indicates moderate frailty. However, individuals with moderate frailty demonstrate heterogeneity in basic activities of daily living (bADL). We aimed to determine if bADL dependency in CFS level 6 predicts poorer outcomes, and to examine the utility of subgrouping the CFS level 6 in predicting mortality and institutionalisation Methods: Records of 201 hospitalised frail patients (mean age, 89.5±4.7 years) were retrospectively reviewed, tients in CFS level 6 (n=106) were examined in terms of the Katz Index (KI) and adverse outcomes. The predictive performance of CFS against modified CFS version 1 (level 6A: CFS level 6 and KI of a2; level 6B: CFS level 6 and KI of si) and modified CFS version 2 (level 6A: CFS level 6 and KI of a2; level 6B1: CFS level 6, KI of si, and feeding independent; level 6B2: CFS level 6, KI of si, and feeding dependent) was compared. Multivariate analysis was used to compare each version in terms of mortality and institutionalisation. The area under the receiver operating characteristic curve (AUQ and the optimal cut-off for each version were determined. Results: In CFS level 6, KI of si was associated with higher 12-month mortality (39.3% vs 15.6%, p=0.01). Among KI items, feeding dependent predicted 12-month mortality (p<0.05). Using the modified CFS version 1, level 6A did not increase the 12-month mortality, compared with CFS level 5 (odds ratio [OR]=1.83, 95% confidence interval [Cl] =0.526.47), unlike level 6B (OR=6.33, 95% CI=2.07-19.33). The modified CFS version 2 produced higher mortality in level 6B1 (OR=5.19, 95% 0=1.30-20.69) and level 6B2 (OR=6.92, 95% 0=2.14-22.35). Similar observations were seen for institutionalisation. The optimal cut-off for 12-month mortality was level 6 for CFS, level 6B for modified CFS version 1, and level 6B1 for modified CFS version 2. Conclusion: This proof-of-concept study confirms the heterogeneity of bADL in moderately frail individuals and validates the use of the modified CFS to subgroup the bADL in level 6 to improve predictive performance.

Objective: Routine health records can be of great value in epidemiological and genetic studies if they are able to reliably identify true disease cases, especially when linked to large cohort studies. Little research has been undertaken into whether coding within UK electronic health records (EHR) is able to accurately identify clinical disease cases of common hand conditions. There is therefore a relative paucity of hand surgical research using EHRs due to concerns that cases cannot be accurately identified. The aim of this study was to investigate the accuracy of hospital and primary care coding of routine EHRs for carpal tunnel syndrome (CTS) and base of thumb osteoarthritis (BTOA). Self-reported disease state as recorded in UK Biobank, a large prospective cohort study was also investigated. Methods: Code lists for each condition were generated by a team of clinicians, clinical coders and epidemiologists. All patients recruited to UK Biobank in one geographical region (Lothian, Scotland) where linked primary and secondary care coded datasets available were included. A decision-making algorithm was designed to define an administratively-confirmed or a clinically confirmed disease case. Patient electronic medical records (EMRs) were independently interrogated by two clinicians and inter-observer reliability calculated. Results: Of the 17,201 Biobank participants in NHS Lothian, 268 had at least one code for CTS and 82 for BTOA. For CTS, 159 cases were confirmed, 100 cases had insufficient information and 9 cases were refuted. Excluding missing data, the positive predictive value (PPV) for true clinical disease cases was 96% for incident disease (90% for prevalent disease; overall 94%). For BTOA, 27 cases were confirmed, 46 cases had insufficient information, and 9 cases were refuted. Excluding missing data, PPV for incident disease was 81% (prevalent disease 56%, overall PPV 75%). Interrogation of the disease cases with insufficient information noted a large proportion arising from primary care and self-report coding systems. Analyzing code combinations revealed that secondary care codes had the highest PPV for CTS and BTOA, emphasizing a more robust evaluation of PPV for patients requiring hospital based care. Overall, inter-observer reliability was good, with agreement in 90% of cases (Cohen kappa of 0.79) for clinical disease cases in CTS and agreement of 98%, (kappa 0.96) for BTOA. Conclusions: We have demonstrated that coding within UK Biobank is of sufficient quality to enable use of the resource for epidemiological and genetic research into common hand conditions, and that EMRs can be used for manual validation of UK health coding systems. Further work is needed to consider potential regional and interdisciplinary differences in coding practice, in strategies for dealing with missing data in EHRs, and to validate coding of common hand conditions in primary care.