METHODS: LDA was applied to 6,328 Taiwanese clinical patients for classification purposes. Clustering method was used to identify the associated influential symptoms for each severity level.
RESULT: LDA shows only 36 HAICDDS questions are significant to distinguish the 5 severity levels with 80% overall accuracy and it increased to 85.83% when combining normal and MCI groups. Severe dementia patients have the most serious declination in most cognitive and functionality domains, follows by moderate dementia, mild dementia, MCI and normal patients.
CONCLUSION: HAICDDS is a reliable and time-saved diagnosis tool in classifying the severity of dementia before undergoing a more in-depth clinical examination. The modified CDR may be indicated for epidemiological study and provide a solid foundation to develop a machine-learning derived screening instrument to detect dementia symptoms.
METHOD: Post-basic students (staff nurses and medical assistants) were given real life pictures showing the wound and periwound area. The students were asked to classify all pictures according to the HPSC at zero months (before attachment) and after two months of attachment. The images were the same but the answers were never given or discussed after the first test.
RESULTS: A total of 30 post-basic students participated in the study, assessing wound 30 images. The results showed that there was an increase of 25.42% in accuracy of wound assessment using the HSPC after two months of clinical attachment compared to pre-attachment. The reliability of the HPSC in wound assessment 79.87%.
CONCLUSION: Health professionals have to be able to assess and classify wounds accurately to be able to manage them accordingly. Assessment and classifications of the periwound skin are important and need to be validated and integrated as a part of a full wound assessment. With experience and adequate training, health professionals are able to comprehensively assess wounds using the validated tool, to enable effective wound management and treatment, accelerating wound healing and improving the quality of life for patients.
METHODS: The Z Printer 450 (3D Systems, Rock Hill, SC) reprinted 10 sets of models for each category of crowding (mild, moderate, and severe) scanned using a structured-light scanner (Maestro 3D, AGE Solutions, Pisa, Italy). Stone and RP models were measured using digital calipers for tooth sizes in the mesiodistal, buccolingual, and crown height planes and for arch dimension measurements. Bland-Altman and paired t test analyses were used to assess agreement and accuracy. Clinical significance was set at ±0.50 mm.
RESULTS: Bland-Altman analysis showed the mean bias of measurements between the models to be within ±0.15 mm (SD, ±0.40 mm), but the 95% limits of agreement exceeded the cutoff point of ±0.50 mm (lower range, -0.81 to -0.41 mm; upper range, 0.34 to 0.76 mm). Paired t tests showed statistically significant differences for all planes in all categories of crowding except for crown height in the moderate crowding group and arch dimensions in the mild and moderate crowding groups.
CONCLUSIONS: The rapid prototyping models were not clinically comparable with conventional stone models regardless of the degree of crowding.
METHODS: Electronic searches were conducted in the Cochrane Database of Systematic Reviews, Cochrane Central Register of Controlled Trials, MEDLINE (complete), PubMed and Scopus. Eligible studies to be included in this review were cohort studies with participants confirmed by laboratory test for dengue infection and comparison among the different severity of dengue infection by using statistical models. The methodological quality of the paper was assessed by independent reviewers using QUADAS-2.
RESULTS: Twenty-six studies published from 1994 to 2017 were included. Most diagnostic models produced an accuracy of 75% to 80% except one with 86%. Two models predicting severe dengue according to the WHO 2009 classification have 86% accuracy. Both of these logistic regression models were applied during the first three days of illness, and their sensitivity and specificity were 91-100% and 79.3-86%, respectively. Another model which evaluated the 30-day mortality of dengue infection had an accuracy of 98.5%.
CONCLUSION: Although there are several potential predictive or diagnostic models for dengue infection, their limitations could affect their validity. It is recommended that these models be revalidated in other clinical settings and their methods be improved and standardised in future.
METHODS: Electronic databases, including PubMed, EMBASE, Cochrane Library, Science Direct, Google Scholar, were systematically searched from the initiation of the database until 12 December 2020. All relevant studies about smoking and COVID-19 were screened using a set of inclusion and exclusion criteria. The Newcastle-Ottawa Scale was used to assess the methodological quality of eligible articles. Random meta-analyses were conducted to estimate odds ratios (ORs) with 95% confidence interval (CIs). Publication bias was assessed using the funnel plot, Begg's test and Egger's test.
RESULTS: A total of 1248 studies were retrieved and reviewed. A total of 40 studies were finally included for meta-analysis. Both current smoking and former smoking significantly increase the risk of disease severity (OR=1.58; 95% CI: 1.16-2.15, p=0.004; and OR=2.48; 95% CI: 1.64-3.77, p<0.001; respectively) with moderate appearance of heterogeneity. Similarly, current smoking and former smoking also significantly increase the risk of death (OR=1.35; 95% CI: 1.12-1.62, p=0.002; and OR=2.58; 95% CI: 2.15-3.09, p<0.001; respectively) with moderate appearance of heterogeneity. There was no evidence of publication bias, which was tested by the funnel plot, Begg's test and Egger's test.
CONCLUSIONS: Smoking, even current smoking or former smoking, significantly increases the risk of COVID-19 severity and death. Further causational studies on this association and ascertianing the underlying mechanisms of this relation is warranted.