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The spectrum of disease severity and the insidiousness of clinical presentation make it difficult to recognize patients with coronavirus disease 2019 (COVID-19) at higher risk of worse outcomes or death when they are seen in the early phases of the disease. There are now well-established risk factors for worse outcomes in patients with COVID-19. These should be factored in when assessing the prognosis of these patients. However, a more precise prognostic assessment in an individual patient may warrant the use of predictive tools. In this manuscript, we conduct a literature review on the severity of illness scores and biomarkers for the prognosis of patients with COVID-19. Several COVID-19-specific scores have been developed since the onset of the pandemic. Some of them are promising and can be integrated into the assessment of these patients. We also found that the well-known pneumonia severity index (PSI) and CURB-65 (confusion, uremia, respiratory rate, BP, age ≥ 65 years) are good predictors of mortality in hospitalized patients with COVID-19. While neither the PSI nor the CURB-65 should be used for the triage of outpatient versus inpatient treatment, they can be integrated by a clinician into the assessment of disease severity and can be used in epidemiological studies to determine the severity of illness in patient populations. Biomarkers also provide valuable prognostic information and, importantly, may depict the main physiological derangements in severe disease. We, however, do not advocate the isolated use of severity of illness scores or biomarkers for decision-making in an individual patient. Instead, we suggest the use of these tools on a case-by-case basis with the goal of enhancing clinician judgment.

The use of artificial intelligence in healthcare is rapidly growing, with great potential to assist medical professionals in diagnosis and treatment of complex medical conditions. In this case report, the recommendations of a pulmonary consultation are compared to the recommendations from a large language model (LLM), ChatGPT, in regards to a case of non-resolving pneumonia. The recommendations by ChatGPT were found to be consistent with the pulmonary consultation, and no recommendations given were inappropriate. As shown in this case report, early generations of LLM such as ChatGPT were able to provide appropriate recommendations for the management of a patient with a complex medical condition.

Purpose: To assess whether exposure to the 2010 Deepwater Horizon oil spill (DHOS) was related to parents’ self-rated health over time. Design: 3 waves of panel data were drawn from the Gulf Coast Population Impact study (2014) and Resilient Children, Youth, and Communities study (2016, 2018). Setting: Coastal Louisiana communities in high-impact DHOS areas. Participants: Respondents were parents or guardians aged 18 - 84, culled from a probability sample of households with a child aged 4 to 18 (N = 526) at the time of the 2010 DHOS. Measures: Self-rated health was measured at each wave. Self-reported physical exposure to the DHOS, economic exposure to the DHOS, and control variables were measured in 2014. Analysis: We used econometric random effects regression for panel data to assess relationships between DHOS exposures and self-rated health over time, controlling for potentially confounding covariates. Results: Both physical exposure (b = −0.39; P < 0.001) and economic exposure (b = −0.34; P < 0.001) to the DHOS had negative associations with self-rated health over the study period. Physical exposure had a larger effect size. Conclusion: Parents’ physical contact with, and economic disruption from, the 2010 DHOS were tied to long-term diminished health.

Purpose: To assess whether trajectories of children’s physical health problems differ by parental college degree attainment in Louisiana areas highly impacted by the 2010 BP Deepwater Horizon oil spill (BP-DHOS). Design: Three waves of panel data (2014, 2016, and 2018) from the Gulf Coast Population Impact / Resilient Children, Youth, and Communities studies. Setting: BP-DHOS-impacted communities in coastal Louisiana. Participants: Parents of children aged 4-18 in a longitudinal probability sample (n = 392). Measures: Reported child physical health problems from the BP-DHOS, parental college degree attainment, and covariates. Analysis: Linear growth curve models are used to assess initial levels of and the rate of change in child physical unknown. The current study uses 3 waves physical health problems by parental college degree attainment. Explanatory variables are measured at baseline and the outcome variable is measured at all 3 waves. Results: Compared to children of parents without college degrees, children of college graduates had fewer initial health problems in 2014 (b = −.33; p = .02). Yet, this health advantage decreased over time, as indicated by their positive rate of change (b = .22; p = .01), such that the higher education health advantage was not statistically significant by 2018. Conclusion: Children of college graduates experienced a physical health advantage following the BP-DHOS, but this gap closed over time. The closure of the gap was due to the children of college graduates experiencing significant increases in reported health problems over the study period.

The West Kowloon Cultural District Artist Square Bridge (ASB) is a part of Hong Kong’s new art and cultural district development. It forms a primary pedestrian link between the adjacent MTR station and the new development. ASB is a design-and-build project consisting of a 78m clear spanning, 8m wide footbridge, an access lift tower and a 30m long staircase and escalators. The footbridge is a covered, dual steel arch and Vierendeel truss footbridge, that crosses an existing flyover, connecting the station shopping mall to the new development. During construction of ASB, very limited space was available on the new development site to accommodate the construction of ASB. These posed engineering challenges to the design and construction team. This paper presents the design of the ASB structures with particular focus on the main footbridge, and the challenges encountered during the design and construction of ASB and how incremental launching solution was developed to tackle these challenges.