Monitoring real-time junior doctor sentiment from comments on a public social media platform: a retrospective observational study.

OBJECTIVES: To investigate whether sentiment analysis and topic modelling can be used to monitor the sentiment and opinions of junior doctors. DESIGN: Retrospective observational study based on comments on a social media website. SETTING: Every publicly available comment in r/JuniorDoctorsUK on Reddit from 1 January 2018 to 31 December 2021. PARTICIPANTS: 7707 Reddit users who commented in the r/JuniorDoctorsUK subreddit. MAIN OUTCOME MEASURE: Sentiment (scored -1 to +1) of comments compared with results of surveys conducted by the General Medical Council. RESULTS: Average comment sentiment was positive but varied significantly during the study period. Fourteen topics of discussion were identified, each associated with a different pattern of sentiment. The topic with the highest proportion of negative comments was the role of a doctor (38%), and the topic with the most positive sentiment was hospital reviews (72%). CONCLUSION: Some topics discussed in social media are comparable to those queried in traditional questionnaires, whereas other topics are distinctive and offer insight into what themes junior doctors care about. Events during the coronavirus pandemic may explain the sentiment trends in the junior doctor community. Natural language processing shows significant potential in generating insights into junior doctors' opinions and sentiment.

Evaluating the impact of the metastatic spinal cord compression coordinator in a regional cancer network: a prospective, non-randomized pilot study.

BACKGROUND: Extradural metastatic spinal cord compression (MSCC) is a debilitating and potentially irreversible complication of cancer. Delay in treatment could lead to irreversible neurological damage, adverse quality of life and a burden on health care resources. Lack of effective communication between teams has been identified as one of the reasons for delay in treatment. The MSCC coordinator (often a nurse, radiotherapy radiographer or a doctor) is responsible for coordinating the diagnosis and management of patients with MSCC. The role has been shown to streamline service, ensure timely decision-making and improved survival outcomes. However, available data are anecdotal or from limited series presented as abstracts in conferences. In this study, we assessed the impact (time to treatment) of the newly introduced role on the treatment pathway compared to similar period in the preceding year. METHODS: This was a multi-centre, prospective, pilot study carried out in Kent, UK between 1st April to 30th June 2021. Patients were considered eligible if they had magnetic resonance imaging (MRI)-confirmed cauda equina or cord compression. The data prospectively collected include: (I) time from diagnostic imaging to radiotherapy treatment; (II) number of referrals to hospital palliative care (HPC), occupational/physiotherapy (OPH) and community hospice referrals (CHP). A comparative retrospective data for (I) was collected for the same time period in the preceding year. The study outcome assessed was reduction in time from radiological diagnosis of MSCC to receiving radiotherapy. RESULTS: Fifty-eight patients in 2020 and 24 patients in 2021 were included in the dataset. The MSCC coordinator role (introduced in 2021) led to reduction in the time from imaging to treatment (P=0.045). Compared to 2020, there was a shorter mean/median time to treatment, seeing more patients being treated within 24 hours. All hospitals except East Kent Hospitals saw more patients being treated within 24 hours. 7 referrals each made to HPC, OPH and CHP respectively. CONCLUSIONS: Introduction of MSCC coordinator role led to improved time from imaging to radiotherapy treatment. The new service led to engagement with rehabilitative and palliative services. Future work should be done to assess the long-term impact of this role on utilization of support services and patient recovery.

Should RECOVERY have used response adaptive randomisation? Evidence from a simulation study.

BACKGROUND: The Randomised Evaluation of COVID-19 Therapy (RECOVERY) trial is aimed at addressing the urgent need to find effective treatments for patients hospitalised with suspected or confirmed COVID-19. The trial has had many successes, including discovering that dexamethasone is effective at reducing COVID-19 mortality, the first treatment to reach this milestone in a randomised controlled trial. Despite this, it continues to use standard or 'fixed' randomisation to allocate patients to treatments. We assessed the impact of implementing response adaptive randomisation within RECOVERY using an array of performance measures, to learn if it could be beneficial going forward. This design feature has recently been implemented within the REMAP-CAP platform trial. METHODS: Trial data was simulated to closely match the data for patients allocated to standard care, dexamethasone, hydroxychloroquine, or lopinavir-ritonavir in the RECOVERY trial from March-June 2020, representing four out of five arms tested throughout this period. Trials were simulated in both a two-arm trial setting using standard care and dexamethasone, and a four-arm trial setting utilising all above treatments. Two forms of fixed randomisation and two forms of response-adaptive randomisation were tested. In the two-arm setting, response-adaptive randomisation was implemented across both trial arms, whereas in the four-arm setting it was implemented in the three non-standard care arms only. In the two-arm trial, randomisation strategies were performed at the whole trial level as well as within three pre-specified patient subgroups defined by patients' respiratory support level. RESULTS: All response-adaptive randomisation strategies led to more patients being given dexamethasone and a lower mortality rate in the trial. Subgroup specific response-adaptive randomisation reduced mortality rates even further. In the two-arm trial, response-adaptive randomisation reduced statistical power compared to FR, with subgroup level adaptive randomisation exhibiting the largest power reduction. In the four-arm trial, response-adaptive randomisation increased statistical power in the dexamethasone arm but reduced statistical power in the lopinavir arm. Response-adaptive randomisation did not induce any meaningful bias in treatment effect estimates nor did it cause any inflation in the type 1 error rate. CONCLUSIONS: Using response-adaptive randomisation within RECOVERY could have increased the number of patients receiving the optimal COVID-19 treatment during the trial, while reducing the number of patients needed to attain the same study power as the original study. This would likely have reduced patient deaths during the trial and lead to dexamethasone being declared effective sooner. Deciding how to balance the needs of patients within a trial and future patients who have yet to fall ill is an important ethical question for the trials community to address. Response-adaptive randomisation deserves to be considered as a design feature in future trials of COVID-19 and other diseases.

A novel algorithmic approach to generate consensus treatment guidelines in adult acute myeloid leukaemia.

Induction therapy for acute myeloid leukaemia (AML) has changed with the approval of a number of new agents. Clinical guidelines can struggle to keep pace with an evolving treatment and evidence landscape and therefore identifying the most appropriate front-line treatment is challenging for clinicians. Here, we combined drug eligibility criteria and genetic risk stratification into a digital format, allowing the full range of possible treatment eligibility scenarios to be defined. Using exemplar cases representing each of the 22 identified scenarios, we sought to generate consensus on treatment choice from a panel of nine aUK AML experts. We then analysed >2500 real-world cases using the same algorithm, confirming the existence of 21/22 of these scenarios and demonstrating that our novel approach could generate a consensus AML induction treatment in 98% of cases. Our approach, driven by the use of decision trees, is an efficient way to develop consensus guidance rapidly and could be applied to other disease areas. It has the potential to be updated frequently to capture changes in eligibility criteria, novel therapies and emerging trial data. An interactive digital version of the consensus guideline is available.