A Collaborative Approach to Providing Best-Practice Childhood Feeding Guidance

Objective: To examine whether training on Division of Responsibility, provided to members of a childhood feeding collaborative, increased familiarity, knowledge, and confidence in disseminating information to parents of young children. Methods: Training was provided to 47 public health nurses and 22 breastfeeding task force members. A within-group pretest, posttest, follow-up design assessed changes in familiarity, knowledge and confidence. Results: Amongst public health nurses, training resulted in a significant increase in familiarity (P < .001); knowledge that restricting amount of food provided to overweight infants and/or children is inappropriate (P < .05); and that children need frequent exposure to new foods (P < .05). Confidence in disseminating information also significantly increased (P < .001). Conclusions and Implications: Health care providers who counsel parents about childhood feeding practices should be trained to increase familiarity with, knowledge of, and confidence in disseminating best practice feeding guidelines to help ensure provision of consistent, accurate messaging

Remember what you did so you know what to do next

We explore using a moderately sized large language model (GPT-J 6B parameters) to create a plan for a simulated robot to achieve 30 classes of goals in ScienceWorld, a text game simulator for elementary science experiments. Previously published empirical work claimed that large language models (LLMs) are a poor fit (Wang et al., 2022) compared to reinforcement learning. Using the Markov assumption (a single previous step), the LLM outperforms the reinforcement learning-based approach by a factor of 1.4. When we fill the LLM's input buffer with as many prior steps as possible, improvement rises to 3.5x. Even when training on only 6.5% of the training data, we observe a 2.2x improvement over the reinforcement-learning-based approach. Our experiments show that performance varies widely across the 30 classes of actions, indicating that averaging over tasks can hide significant performance issues. In work contemporaneous with ours, Lin et al. (2023) demonstrated a two-part approach (SwiftSage) that uses a small LLM (T5-large) complemented by OpenAI's massive LLMs to achieve outstanding results in ScienceWorld. Our 6-B parameter, single-stage GPT-J matches the performance of SwiftSage's two-stage architecture when it incorporates GPT-3.5 turbo which has 29-times more parameters than GPT-J.Comment: Identical to EMNLP 2023 Finding

Li Wenliang, a face to the frontline healthcare worker? The first doctor to notify the emergence of the SARS-CoV-2 (COVID-19) outbreak

Dr Li Wenliang, who lost his life to the novel coronavirus, SARS-CoV-2, became the face of the threat of SARS-CoV-2 to frontline workers, the clinicians taking care of patients. Li, 34, was an ophthalmologist at Wuhan Central Hospital. On 30th December, 2019, when the Wuhan municipal health service sent out an alert, he reportedly warned a closed group of ex-medical school classmates on the WeChat social media site of “Seven cases of severe acute respiratory syndrome (SARS) like illness with links with the Huanan Seafood Wholesale Market” at his hospital. He was among eight people reprimanded by security officers for “spreading rumours”. In a tragic turn of events, he subsequently contracted SARS-CoV-2 and, after a period in intensive care, died on the morning of Friday 7th February, 2020 (South China Morning Post, 2020). This case is a stark reminder of the risks of emerging disease outbreaks for healthcare workers (HCWs). Dr Li Wenliang’s name is added to the long list of HCW that were at the forefront of outbreaks of SARS, Ebola, MERS and now SARS-CoV-2. It is important to recognise that it was the clinicians in Wuhan who sounded the alarm about the emergence of SARS-CoV-2 which was rapidly identified after these clinicians sent samples to a reference laboratory for next generation sequencing (NGS) (Zhou et al., 2020)

Circulating 25-Hydroxyvitamin D and the Risk of Rarer Cancers: Design and Methods of the Cohort Consortium Vitamin D Pooling Project of Rarer Cancers

The Cohort Consortium Vitamin D Pooling Project of Rarer Cancers (VDPP), a consortium of 10 prospective cohort studies from the United States, Finland, and China, was formed to examine the associations between circulating 25-hydroxyvitamin D (25(OH)D) concentrations and the risk of rarer cancers. Cases (total n = 5,491) included incident primary endometrial (n = 830), kidney (n = 775), ovarian (n = 516), pancreatic (n = 952), and upper gastrointestinal tract (n = 1,065) cancers and non-Hodgkin lymphoma (n = 1,353) diagnosed in the participating cohorts. At least 1 control was matched to each case on age, date of blood collection (1974–2006), sex, and race/ethnicity (n = 6,714). Covariate data were obtained from each cohort in a standardized manner. The majority of the serum or plasma samples were assayed in a central laboratory using a direct, competitive chemiluminescence immunoassay on the DiaSorin LIAISON platform (DiaSorin, Inc., Stillwater, Minnesota). Masked quality control samples included serum standards from the US National Institute of Standards and Technology. Conditional logistic regression analyses were conducted using clinically defined cutpoints, with 50–<75 nmol/L as the reference category. Meta-analyses were also conducted using inverse-variance weights in random-effects models. This consortium approach permits estimation of the association between 25(OH)D and several rarer cancers with high accuracy and precision across a wide range of 25(OH)D concentrations