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Background: The intensive care unit (ICU) stay has been linked with a number of physical and psychological sequelae, known collectively as post-intensive care syndrome (PICS). Specific ICU follow-up services are relatively recent developments in health systems, and may have the potential to address PICS through targeting unmet health needs arising from the experience of the ICU stay. There is currently no single accepted model of follow-up service and current aftercare programmes encompass a variety of interventions and materials. There is uncertain evidence about whether follow-up services effectively address PICS, and this review assesses this.
Objectives: Our main objective was to assess the effectiveness of follow-up services for ICU survivors that aim to identify and address unmet health needs related to the ICU period. We aimed to assess effectiveness in relation to health-related quality of life (HRQoL), mortality, depression and anxiety, post-traumatic stress disorder (PTSD), physical function, cognitive function, ability to return to work or education and adverse effects.Our secondary objectives were to examine different models of follow-up services. We aimed to explore: the effectiveness of service organisation (physician- versus nurse-led, face-to-face versus remote, timing of follow-up service); differences related to country (high-income versus low- and middle-income countries); and effect of delirium, which can subsequently affect cognitive function, and the effect of follow-up services may differ for these participants.
Selection criteria: We included randomised and non-randomised studies with adult participants, who had been discharged from hospital following an ICU stay. We included studies that compared an ICU follow-up service using a structured programme and co-ordinated by a healthcare professional versus no follow-up service or standard care.
Main results: We included five studies (four randomised studies; one non-randomised study), for a total of 1707 participants who were ICU survivors with a range of illness severities and conditions. Follow-up services were led by nurses in four studies or a multidisciplinary team in one study. They included face-to-face consultations at home or in a clinic, or telephone consultations or both. Each study included at least one consultation (weekly, monthly, or six-monthly), and two studies had up to eight consultations. Although the design of follow-up service consultations differed in each study, we noted that each service included assessment of participants' needs with referrals to specialist support if required.It was not feasible to blind healthcare professionals or participants to the intervention and we did not know whether this may have introduced performance bias. We noted baseline differences (two studies), and services included additional resources (two studies), which may have influenced results, and one non-randomised study had high risk of selection bias.We did not combine data from randomised studies with data from one non-randomised study. Follow-up services for improving long-term outcomes in ICU survivors may make little or no difference to HRQoL at 12 months (standardised mean difference (SMD) -0.0, 95% confidence interval (CI) -0.1 to 0.1; 1 study; 286 participants; low-certainty evidence). We found moderate-certainty evidence from five studies that they probably also make little or no difference to all-cause mortality up to 12 months after ICU discharge (RR 0.96, 95% CI 0.76 to 1.22; 4 studies; 1289 participants; and in one non-randomised study 79/259 deaths in the intervention group, and 46/151 in the control group) and low-certainty evidence from four studies that they may make little or no difference to PTSD (SMD -0.05, 95% CI -0.19 to 0.10, 703 participants, 3 studies; and one non-randomised study reported less chance of PTSD when a follow-up service was used).It is uncertain whether using a follow-up service reduces depression and anxiety (3 studies; 843 participants), physical function (4 studies; 1297 participants), cognitive function (4 studies; 1297 participants), or increases the ability to return to work or education (1 study; 386 participants), because the certainty of this evidence is very low. No studies measured adverse effects.We could not assess our secondary objectives because we found insufficient studies to justify subgroup analysis.
Authors' conclusions: We found insufficient evidence, from a limited number of studies, to determine whether ICU follow-up services are effective in identifying and addressing the unmet health needs of ICU survivors. We found five ongoing studies which are not included in this review; these ongoing studies may increase our certainty in the effect in future updates. Because of limited data, we were unable to explore whether one design of follow-up service is preferable to another, or whether a service is more effective for some people than others, and we anticipate that future studies may also vary in design. We propose that future studies are designed with robust methods (for example randomised studies are preferable) and consider only one variable (the follow-up service) compared to standard care; this would increase confidence that the effect is due to the follow-up service rather than concomitant therapies.
The CDC and FDA require additional information on selected VAERS reports for the public health purpose of helping to ensure the safety of U.S. licensed vaccines. You or your health care provider may be contacted for follow-up information by VAERS staff after your report is received. These selected reports are followed up by a team of health care professionals to obtain additional information (such as medical records and autopsy reports) to provide as complete a picture of the case as possible.
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Making language models bigger does not inherently make them better at following a user's intent. For example, large language models can generate outputs that are untruthful, toxic, or simply not helpful to the user. In other words, these models are not aligned with their users. In this paper, we show an avenue for aligning language models with user intent on a wide range of tasks by fine-tuning with human feedback. Starting with a set of labeler-written prompts and prompts submitted through a language model API, we collect a dataset of labeler demonstrations of the desired model behavior, which we use to fine-tune GPT-3 using supervised learning. We then collect a dataset of rankings of model outputs, which we use to further fine-tune this supervised model using reinforcement learning from human feedback. We call the resulting models InstructGPT. In human evaluations on our prompt distribution, outputs from the 1.3B parameter InstructGPT model are preferred to outputs from the 175B GPT-3, despite having 100x fewer parameters. Moreover, InstructGPT models show improvements in truthfulness and reductions in toxic output generation while having minimal performance regressions on public NLP datasets. Even though InstructGPT still makes simple mistakes, our results show that fine-tuning with human feedback is a promising direction for aligning language models with human intent.
We understand that our BMT survivors may feel anxious about COVID-19. We are taking measures to make our clinic visits safe through additional screening and COVID-19 testing. We are following all guidance about personal protective equipment and additional cleaning and are confident that our actions are making a visit to the clinic safe for patients and staff. There are new practices in place that you will be asked to follow if you have an appointment. 17dc91bb1f
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