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The biodiversity of coral reef habitats is rapidly declining due to the effects of anthropogenic climate change, prompting the use of active restoration as a mitigation strategy. Sexual propagation can maintain or enhance genetic diversity in restoration of these ecosystems, but these approaches suffer from a range of inefficiencies in rearing and husbandry. Algal overgrowth of juveniles is a major bottleneck in the production of sexually propagated corals that may be alleviated by co-culture with herbivores. We reared juvenile Montipora capitata alongside juvenile native Hawaiian collector urchins, Tripneustes gratilla, for 15 weeks and documented significant ecological benefits of co-culture. Urchin treatments significantly increased the survivorship of coral aggregates (14%) and individual settlers (24%). We also documented a significant increase in coral growth in the presence of urchins. These results demonstrate the utility of microherbivory in promoting coral growth and survivorship in ex situ conditions, providing valuable insight for restoration pipelines of native Hawaiian coral species.

Abstract Background Loss of arm function is a common and distressing consequence of stroke. We describe the protocol for a pragmatic, multicentre randomised controlled trial to determine whether robot-assisted training improves upper limb function following stroke. Methods/design Study design: a pragmatic, three-arm, multicentre randomised controlled trial, economic analysis and process evaluation. Setting: NHS stroke services. Participants: adults with acute or chronic first-ever stroke (1 week to 5 years post stroke) causing moderate to severe upper limb functional limitation. Randomisation groups: 1. Robot-assisted training using the InMotion robotic gym system for 45 min, three times/week for 12 weeks 2. Enhanced upper limb therapy for 45 min, three times/week for 12 weeks 3. Usual NHS care in accordance with local clinical practice Randomisation: individual participant randomisation stratified by centre, time since stroke, and severity of upper limb impairment. Primary outcome: upper limb function measured by the Action Research Arm Test (ARAT) at 3 months post randomisation. Secondary outcomes: upper limb impairment (Fugl-Meyer Test), activities of daily living (Barthel ADL Index), quality of life (Stroke Impact Scale, EQ-5D-5L), resource use, cost per quality-adjusted life year and adverse events, at 3 and 6 months. Blinding: outcomes are undertaken by blinded assessors. Economic analysis: micro-costing and economic evaluation of interventions compared to usual NHS care. A within-trial analysis, with an economic model will be used to extrapolate longer-term costs and outcomes. Process evaluation: semi-structured interviews with participants and professionals to seek their views and experiences of the rehabilitation that they have received or provided, and factors affecting the implementation of the trial. Sample size: allowing for 10% attrition, 720 participants provide 80% power to detect a 15% difference in successful outcome between each of the treatment pairs. Successful outcome definition: baseline ARAT 0–7 must improve by 3 or more points; baseline ARAT 8–13 improve by 4 or more points; baseline ARAT 14–19 improve by 5 or more points; baseline ARAT 20–39 improve by 6 or more points. Discussion The results from this trial will determine whether robot-assisted training improves upper limb function post stroke. Trial registration ISRCTN, identifier: ISRCTN69371850 . Registered 4 October 2013.

Abstract Background Recruitment to randomised prevention trials is challenging, not least for intracerebral haemorrhage (ICH) associated with antithrombotic drug use. We investigated reasons for not recruiting apparently eligible patients at hospital sites that keep screening logs in the ongoing REstart or STop Antithrombotics Randomised Trial (RESTART), which seeks to determine whether to start antiplatelet drugs after ICH. Method By the end of May 2015, 158 participants had been recruited at 108 active sites in RESTART. The trial coordinating centre invited all sites that kept screening logs to submit screening log data, followed by one reminder. We checked the integrity of data, focused on the completeness of data about potentially eligible patients and categorised the reasons they were not randomised. Results Of 108 active sites, 39 (36%) provided usable screening log data over a median of ten (interquartile range = 5–13) months of recruitment per site. During this time, sites screened 633 potentially eligible patients and randomised 53 (8%) of them. The main reasons why 580 patients were not randomised were: 43 (7%) patients started anticoagulation, 51 (9%) patients declined, 148 (26%) patients’ stroke physicians were not uncertain about using antiplatelet drugs, 162 (28%) patients were too unwell and 176 (30%) patients were not randomised due to other reasons. Conclusion RESTART recruited ~8% of eligible patients. If more physicians were uncertain about the therapeutic dilemma that RESTART is addressing, RESTART could have recruited up to four times as many participants. The trial coordinating centre continues to engage with physicians about their uncertainty. Trial registration EU Clinical Trials, EudraCT 2012-003190-26 . Registered on 3 July 2012.

Background: Loss of arm function is common after stroke. Robot-assisted training may improve arm outcomes. Objective: The objectives were to determine the clinical effectiveness and cost-effectiveness of robot-assisted training, compared with an enhanced upper limb therapy programme and with usual care. Design: This was a pragmatic, observer-blind, multicentre randomised controlled trial with embedded health economic and process evaluations. Setting: The trial was set in four NHS trial centres. Participants: Patients with moderate or severe upper limb functional limitation, between 1 week and 5 years following first stroke, were recruited. Interventions: Robot-assisted training using the Massachusetts Institute of Technology-Manus robotic gym system (InMotion commercial version, Interactive Motion Technologies, Inc., Watertown, MA, USA), an enhanced upper limb therapy programme comprising repetitive functional task practice, and usual care. Main outcome measures: The primary outcome was upper limb functional recovery ‘success’ (assessed using the Action Research Arm Test) at 3 months. Secondary outcomes at 3 and 6 months were the Action Research Arm Test results, upper limb impairment (measured using the Fugl-Meyer Assessment), activities of daily living (measured using the Barthel Activities of Daily Living Index), quality of life (measured using the Stroke Impact Scale), resource use costs and quality-adjusted life-years. Results: A total of 770 participants were randomised (robot-assisted training, n = 257; enhanced upper limb therapy, n = 259; usual care, n = 254). Upper limb functional recovery ‘success’ was achieved in the robot-assisted training [103/232 (44%)], enhanced upper limb therapy [118/234 (50%)] and usual care groups [85/203 (42%)]. These differences were not statistically significant; the adjusted odds ratios were as follows: robot-assisted training versus usual care, 1.2 (98.33% confidence interval 0.7 to 2.0); enhanced upper limb therapy versus usual care, 1.5 (98.33% confidence interval 0.9 to 2.5); and robot-assisted training versus enhanced upper limb therapy, 0.8 (98.33% confidence interval 0.5 to 1.3). The robot-assisted training group had less upper limb impairment (as measured by the Fugl-Meyer Assessment motor subscale) than the usual care group at 3 and 6 months. The enhanced upper limb therapy group had less upper limb impairment (as measured by the Fugl-Meyer Assessment motor subscale), better mobility (as measured by the Stroke Impact Scale mobility domain) and better performance in activities of daily living (as measured by the Stroke Impact Scale activities of daily living domain) than the usual care group, at 3 months. The robot-assisted training group performed less well in activities of daily living (as measured by the Stroke Impact Scale activities of daily living domain) than the enhanced upper limb therapy group at 3 months. No other differences were clinically important and statistically significant. Participants found the robot-assisted training and the enhanced upper limb therapy group programmes acceptable. Neither intervention, as provided in this trial, was cost-effective at current National Institute for Health and Care Excellence willingness-to-pay thresholds for a quality-adjusted life-year. Conclusions: Robot-assisted training did not improve upper limb function compared with usual care. Although robot-assisted training improved upper limb impairment, this did not translate into improvements in other outcomes. Enhanced upper limb therapy resulted in potentially important improvements on upper limb impairment, in performance of activities of daily living, and in mobility. Neither intervention was cost-effective. Future work: Further research is needed to find ways to translate the improvements in upper limb impairment seen with robot-assisted training into improvements in upper limb function and activities of daily living. Innovations to make rehabilitation programmes more cost-effective are required. Limitations: Pragmatic inclusion criteria led to the recruitment of some participants with little prospect of recovery. The attrition rate was higher in the usual care group than in the robot-assisted training or enhanced upper limb therapy groups, and differential attrition is a potential source of bias. Obtaining accurate information about the usual care that participants were receiving was a challenge. Trial registration: Current Controlled Trials ISRCTN69371850. Funding: This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 24, No. 54. See the NIHR Journals Library website for further project information.

Background: Seven-day working in hospitals is a current priority of international health research and policy. Previous research has shown variability in delivering evidence-based clinical interventions across different times of the day and week. We aimed to identify factors influencing such variations in London hyperacute stroke units. Objectives: To investigate variations in quality of acute stroke care and outcomes by day and time of admission in London hyperacute stroke units, and to identify factors influencing such variations. Design: This was a prospective cohort study using anonymised patient-level data from the Sentinel Stroke National Audit Programme. Factors influencing variations in care and outcomes were studied through interview and observation data. Setting: The setting was acute stroke services in London hyperacute stroke units. Participants: A total of 7094 patients with a primary diagnosis of stroke took part. We interviewed hyperacute stroke unit staff (n = 76), including doctors, nurses, therapists and administrators, and 31 patients and carers. We also conducted non-participant observations of delivery of care at different times of the day and week (n = 45, ≈102 hours). Intervention: Hub-and-spoke model for care of suspected acute stroke patients in London with performance standards was designed to deliver uniform access to high-quality hyperacute stroke unit care across the week. Main outcome measures: Indicators of quality of acute stroke care, mortality at 3 days after admission, disability at the end of the inpatient spell and length of stay. Data sources: Sentinel Stroke National Audit Programme data for all patients in London hyperacute stroke units with a primary diagnosis of stroke between 1 January and 31 December 2014, and nurse staffing data for all eight London hyperacute stroke units for the same period. Results: We found no variation in quality of care by day and time of admission across the week in terms of stroke nursing assessment, brain scanning and thrombolysis in London hyperacute stroke units, nor in 3-day mortality nor disability at hospital discharge. Other quality-of-care measures significantly varied by day and time of admission. Quality of care was better if the nurse in charge was at a higher band and/or there were more nurses on duty. Staff deliver ‘front-door’ interventions consistently by taking on additional responsibilities out of hours, creating continuities between day and night, building trusting relationships and prioritising ‘front-door’ interventions. Limitations: We were unable to measure long-term outcomes as our request to the Sentinel Stroke National Audit Programme, the Healthcare Quality Improvement Partnership and NHS Digital for Sentinel Stroke National Audit Programme data linked with patient mortality status was not fulfilled. Conclusions: Organisational factors influence 24 hours a day, 7 days a week (24/7), provision of stroke care, creating temporal patterns of provision reflected in patient outcomes, including mortality, length of stay and functional independence. Future work: Further research would help to explore 24/7 stroke systems in other contexts. We need a clearer understanding of variations by looking at absolute time intervals, rather than achievement of targets. Research is needed with longer-term mortality and modified Rankin Scale data, and a more meaningful range of outcomes. Funding: This project was funded by the National Institute for Health Research (NIHR) Health Services and Delivery Research programme and will be published in full in Health Services and Delivery Research; Vol. 8, No. 34. See the NIHR Journals Library website for further project information.

Advances in artificial intelligence (AI) have been changing the landscape in daily life and the practice of medicine. As these tools have evolved to become consumer-friendly, AI has become more accessible to many individuals, including applicants to medical school. With the rise of AI models capable of generating complex passages of text, questions have arisen regarding the appropriateness of using such tools to assist in the preparation of medical school applications. In this commentary, the authors offer a brief history of AI tools in medicine and describe large language models, a form of AI capable of generating natural language text passages. They question whether AI assistance should be considered inappropriate in preparing applications and compare it with the assistance some applicants receive from family, physician friends, or consultants. They call for clearer guidelines on what forms of assistance-human and technological-are permitted in the preparation of medical school applications. They recommend that medical schools steer away from blanket bans on AI tools in medical education and instead consider mechanisms for knowledge sharing about AI between students and faculty members, incorporation of AI tools into assignments, and the development of curricula to teach the use of AI tools as a competency., (Copyright © 2023 by the Association of American Medical Colleges.)