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More than 80% of coronavirus disease 2019 (COVID-19) cases are mild or moderate. In this study, a risk model was developed for predicting rehabilitation duration (the time from hospital admission to discharge) of the mild-moderate COVID-19 cases and was used to conduct refined risk management for different risk populations.
A total of 90 consecutive patients with mild-moderate COVID-19 were enrolled. Large-scale datasets were extracted from clinical practices. Through the multivariable linear regression analysis, the model was based on significant risk factors and was developed for predicting the rehabilitation duration of mild-moderate cases of COVID-19. To assess the local epidemic situation, risk management was conducted by weighing the risk of populations at different risk.
Ten risk factors from 44 high-dimensional clinical datasets were significantly correlated to rehabilitation duration (P < 0.05). Among these factors, 5 risk predictors were incorporated into a risk model. Individual rehabilitation durations were effectively calculated. Weighing the local epidemic situation, threshold probability was classified for low risk, intermediate risk, and high risk. Using this classification, risk management was based on a treatment flowchart tailored for clinical decision-making.
The proposed novel model is a useful tool for individualized risk management of mild-moderate COVID-19 cases, and it may readily facilitate dynamic clinical decision-making for different risk populations.
The median duration of hospital stays due to COVID-19 has been reported in several studies on China as 10−13 days. Global studies have indicated that the length of hospitalisation depends on different factors, such as the time elapsed from exposure to symptom onset, and from symptom onset to hospital admission, as well as specificities of the country under study. The goal of this paper is to identify factors associated with the median duration of hospital stays of COVID-19 patients during the second COVID-19 wave that hit Vietnam from 5 March to 8 April 2020.
We used retrospective data on 133 hospitalised patients with COVID-19 recorded over at least two weeks during the study period. The Cox proportional-hazards regression model was applied to determine the potential risk factors associated with length of hospital stay.
There were 65 (48.9%) females, 98 (73.7%) patients 48 years old or younger, 15 (11.3%) persons with comorbidities, 21 (16.0%) severely ill patients and 5 (3.8%) individuals with life-threatening conditions. Eighty-two (61.7%) patients were discharged after testing negative for the SARS-CoV-2 virus, 51 were still in the hospital at the end of the study period and none died. The median duration of stay in a hospital was 21 (IQR: 16–34) days. The multivariable Cox regression model showed that age, residence and sources of contamination were significantly associated with longer duration of hospitalisation.
A close look at how long COVID-19 patients stayed in the hospital could provide an overview of their treatment process in Vietnam, and support the country's National Steering Committee on COVID-19 Prevention and Control in the efficient allocation of resources over the next stages of the COVID-19 prevention period.
Probing and controlling electrons and nuclei in matter at the attosecond timescale became possible with the generation of attosecond pulses by few-cycle intense lasers, and has revolutionized our understanding of atomic structure and molecular processes. This book provides an intuitive approach to this emerging field, utilizing simplified models to develop a clear understanding of how matter interacts with attosecond pulses of light. An introductory chapter outlines the structure of atoms and molecules and the properties of a focused laser beam. Detailed discussion of the fundamental theory of attosecond and strong-field physics follows, including the molecular tunnelling ionization model (MO-ADK theory), the quantitative rescattering (QRS) model, and the laser induced electronic diffraction (LIED) theory for probing the change of atomic configurations in a molecule. Highlighting the cutting-edge developments in attosecond and strong field physics, and identifying future opportunities and challenges, this self-contained text is invaluable for students and researchers in the field.
The lengthy and complex focal article by Tett, Hundley, and Christiansen (2017) is based on a fundamental misunderstanding of the nature of validity generalization (VG): It is based on the assumption that what is generalized in VG is the estimated value of mean rho (
). This erroneous assumption is stated repeatedly throughout the article. A conclusion of validity generalization does not imply that
is identical across all situations. If VG is present, most, if not all, validities in the validity distribution are positive and useful even if there is some variation in that distribution. What is generalized is the entire distribution of rho (
), not just the estimated
or any other specific value of validity included in the distribution. This distribution is described by its mean (
) and standard deviation (SDρ). A helpful concept based on these parameters (assuming ρ is normally distributed) is the credibility interval, which reflects the range where most of the values of ρ can be found. The lower end of the 80% credibility interval (the 90% credibility value, CV =
– 1.28 × SDρ) is used to facilitate understanding of this distribution by indicating the statistical “worst case” for validity, for practitioners using VG. Validity has an estimated 90% chance of lying above this value. This concept has long been recognized in the literature (see Hunter & Hunter, 1984, for an example; see also Schmidt, Law, Hunter, Rothstein, Pearlman, & McDaniel, 1993, and hundreds of VG articles that have appeared in the literature over the past 40 years since the invention of psychometric meta-analysis as a means of examining VG [Schmidt & Hunter, 1977]). The
is the value in the distribution with the highest likelihood of occurring (although often by only a small amount), but it is the whole distribution that is generalized. Tett et al. (2017) state that some meta-analysis articles claim that they are generalizing only
. If true, this is inappropriate. Because
has the highest likelihood in the ρ distribution, discussion often focuses on that value as a matter of convenience, but
is not what is generalized in VG. What is generalized is the conclusion that there is validity throughout the credibility interval. The false assumption that it is
and not the ρ distribution as a whole that is generalized in VG is the basis for the Tett et al. article and is its Achilles heel. In this commentary, we examine the target article's basic arguments and point out errors and omissions that led Tett et al. to falsely conclude that VG is a “myth.”