In testing a general linear hypothesis of the form K ∈ W under a general linear model, an equivalent hypothesis involving only estimable parametric functions is provided, and then an explicit test statistic in terms of the model matrices is given.The corresponding results are expanded to the case of a general linear model with a restriction and are illustrated by an example.
While COVID-19 vaccines are generally available, not all people receive vaccines. To reach herd immunity, most of a population must be vaccinated. It is, thus, important to identify factors influencing people’s vaccination preferences, as knowledge of these preferences allows for governments and health programs to increase their vaccine coverage more effectively. Fortunately, vaccination data were collected by U.S. Census Bureau in partnership with the CDC via the Household Pulse Survey (HPS) for Americans. This study presents the first analysis of the 24 vaccination datasets collected by the HPS from January 2021 to May 2022 for 250 million respondents of different ages, genders, sexual orientations, races, education statuses, marital statuses, household sizes, household income levels, and resources used for spending needs, and with different reasons for not receiving or planning to receive a vaccine. Statistical analysis techniques, including an analysis of variance (ANOVA), Tukey multiple comparisons test, and hierarchical clustering (HC), were implemented to analyze the HPS vaccination data in the R language. It was found that sexual orientation, gender, age, and education had statistically significant influences on the vaccination rates. In particular, the gay/lesbian group showed a higher vaccination rate than the straight group; the transgender group had a lower vaccination rate than either the female or the male groups; older respondents showed greater preference for vaccination; respondents with higher education levels also preferred vaccination. As for the other factors that were not significant enough to influence vaccinations in the ANOVA, notable trends were found. Asian Americans had higher vaccination rates than other races; respondents from larger household sizes had a lower chance of getting vaccinated; the unmarried group showed the lowed vaccination rate in the marital category; the respondents depending on borrowed money from the Supplemental Nutrition Assistance Program (SNAP) showed a lower vaccination rate than people with regular incomes. Concerns regarding the side-effects and the safety of the vaccines were the two major reasons for vaccination hesitance at the beginning of the pandemic, while having no trust in the vaccines and no trust in the government became more common in the later stage of the pandemic. The findings in this study can be used by governments or organizations to improve their vaccination campaigns or methods of combating future pandemics.
Purpose Increased operative time can be due to patient, surgeon and surgical factors, and may be predicted by machine learning (ML) modeling to potentially improve staf utilization and operating room eiciency. The purposes of our study were to: (1) determine how demographic, surgeon, and surgical factors afected operative times, and (2) train a ML model to estimate operative time for robotic-assisted primary total knee arthroplasty (TKA). Methods A retrospective study from 2007 to 2020 was conducted including 300,000 unilateral primary TKA cases. Demographic and surgical variables were evaluated using Wilcoxon/Kruskal-Wallis tests to determine signiicant factors of operative time as predictors in the ML models. For the ML analysis of robotic-assisted TKAs (> 18,000), two algorithms were used to learn the relationship between selected predictors and operative time. Predictive model performance was subsequently assessed on a test data set comparing predicted and actual operative time. Root mean square error (RMSE), R 2 and percentage of predictions with an error < 5/10/15 min were computed. Results Males, BMI > 40 kg/m 2 and cemented implants were associated with increased operative time, while age > 65yo, cementless, and high surgeon case volume had reduced operative time. Robotic-assisted TKA increased operative time for low-volume surgeons and decreased operative time for high-volume surgeons. Both ML models provided more accurate operative time predictions than standard time estimates based on surgeon historical averages. Conclusions This study demonstrated that greater surgeon case volume, cementless ixation, manual TKA, female, older and non-obese patients reduced operative time. ML prediction of operative time can be more accurate than historical averages, which may lead to optimized operating room utilization. Level of evidence III.
Many studies involving robotic-assisted total knee arthroplasty (RATKA) have demonstrated superiority regarding soft tissue balance and consistency with alignment target achievement. However, studies investigating whether RATKA is associated with improved patient outcomes regarding physical function and pain are also important. Therefore, we performed a cluster analysis and examined factors that contributed to differences in patient-reported outcome measures (PROMs). Specifically, we analyzed: (1) reduced WOMAC (rWOMAC) scores regarding pain and function; (2) usage of RATKA; (3) common patient comorbidities; as well as (4) patient demographic factors. The rWOMAC score is an abbreviated PROM that includes pain and physical function domains. This study analyzed 853 patients (95 conventional and 758 robotic-assisted) who had completed preoperative, 6-month, and 1-year postoperative rWOMAC surveys. Two clusters were constructed using rWOMAC pain and function scores at 1 year. Cluster 1 included 753 patients who had better outcomes at 1 year (mean rWOMAC pain = 0.9, mean rWOMAC function = 1.4), and cluster 2 included 100 patients who had worse outcomes at 1 year (mean rWOMAC pain = 7.7, mean rWOMAC function = 10.4). The clusters were compared to determine (1) how scores improved and (2) what patient characteristics were significantly different between clusters. Cluster 1 demonstrated greater improvement from preoperative to 6 months or 1 year (p = 0.0013 for pain preoperative to 6 months, p< 0.0001 for other measures) and 6 months to 1 year (p< 0.0001). Comparisons demonstrated that cluster 1 had older patients (67 vs. 65 years, p = 0.0479) who had lower body mass index or BMIs (31.8 vs. 33.9 kg/m2, p = 0.0042) and no significant differences in sex (p = 0.7849). Cluster 1 also had a significantly higher percentage of RATKA patients (90 vs. 79%, p< 0.001). Cluster analyses provided differentiating factors which were associated with improved postoperative rWOMAC pain and function scores at 1 year. Patients undergoing robotic-assisted TKA were associated with better rWOMAC pain and function scores from preoperative to 6 months and 1 year.
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