ResultsRecruitment and retention The study app was downloaded by 13,207 users over the 12-month recruitment period ( Figs 1 and 2a) with recruitment from all 124 UK postcode areas.They have supported the interpretation of findings and the development of dissemination plans for the results, ensuring the results reach study participants, patient organizations and the general public. and other members of the Patient and Public Involvement Group were involved in media broadcasts at study launch and subsequent public engagement activities, explaining why the research question was important to them and relevant to patients with long-term pain conditions. MethodsPatient involvement Patient involvement has been important throughout the study, from inception to interpretation of the results.Collecting this kind of multi-faceted data in large populations over long periods of time, has been difficult.Such data need to include other factors potentially linked to daily pain variation and weather, such as mood and amount of physical activity.Resolving this question requires collection of high-quality symptom and weather data on large numbers of individuals
Studies have failed to reach consensus in part due to their small sample sizes or short durations by considering a limited range of weather conditions and heterogeneity in study design.IntroductionWeather has been thought to affect symptoms in patients with chronic disease since the time of Hippocrates over 2000 years ago.Such an increased risk may be meaningful to people living with chronic pain.The odds of a pain event was 12% higher per one standard deviation increase in relative humidity (9 percentage points) ( OR 1.119 (1.084–1.154), compared to 4% lower for pressure ( OR 0.958 (0.930–0.989) and 4% higher for wind speed ( OR 1.041 (1.010–1.073) (11 mbar and 2 m s−1, respectively).The ‘worst’ combination of weather variables would increase the odds of a pain event by just over 20% compared to an average day.The effect of weather on pain was not fully explained by its day-to-day effect on mood or physical activity.The most significant contribution was from relative humidity.This study has demonstrated that higher relative humidity and wind speed, and lower atmospheric pressure, were associated with increased pain severity in people with long-term pain conditions.Weather has been thought to affect symptoms in patients with chronic disease since the time of Hippocrates over 2000 years ago.
Going forward, we are researching better ways to assist humans in evaluating model behavior, with the goal of finding techniques that scale to aligning artificial general intelligence. Our progress on book summarization is the first large-scale empirical work on scaling alignment techniques. In this case, to evaluate book summaries we empower humans with individual chapter summaries written by our model, which saves them time when evaluating these summaries relative to reading the source text. Our current approach to this problem is to empower humans to evaluate machine learning model outputs using assistance from other models. Therefore we want our ability to evaluate our models to increase as their capabilities increase. This makes it harder to detect subtle problems in model outputs that could lead to negative consequences when these models are deployed. This work is part of our ongoing research into aligning advanced AI systems, which is key to our mission. As we train our models to do increasingly complex tasks, making informed evaluations of the models’ outputs will become increasingly difficult for humans. Our method can be used to summarize books of unbounded length, unrestricted by the context length of the transformer models we use.See for yourself on our summary explorer! For example, you can trace to find where in the original text certain events from the summary happen. It is easier to trace the summary-writing process.Decomposition allows humans to evaluate model summaries more quickly by using summaries of smaller parts of the book rather than reading the source text.
Compared to an end-to-end training procedure, recursive task decomposition has the following advantages: In this case we break up summarizing a long piece of text into summarizing several shorter pieces. To address this problem, we additionally make use of recursive task decomposition: we procedurally break up a difficult task into easier ones. But judging summaries of entire books takes a lot of effort to do directly since a human would need to read the entire book, which takes many hours. In the past we found that training a model with reinforcement learning from human feedback helped align model summaries with human preferences on short posts and articles. Large pretrained models aren’t very good at summarization. Consider the task of summarizing a piece of text.
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