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Best Database Comparison Statistics
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- Directed networks are often assessed also according to their bow tie structure 31 Table 1 Descriptive statistics and field decompositions of citation and other networks. [0]
- The residuals are listed in decreasing order, while the shaded regions are 95% and 99% confidence intervals of independent Student t. [0]
- Panel shows the residuals of merely independent statistics, where the shaded region is 95% confidence interval. [0]
- The health worker occupations are classified according to the latest version of the International Standard Classification of Occupations. [1]
- Employment rate in OECD rises to 68.7% in Q4 ’21. [2]
- news.•Unemployment rate in the OECD area drops below the pre pandemic rate to 5.2% in 2024. [2]
- Covariates obtained by stepwise regression from 80% of randomly selected patients were used to develop algorithms. [3]
- To compare the performances of these algorithms, the mean percentage of patients whose predicted dose fell within 20% of the actual dose (mean percentage within 20%). [3]
- mg/week, mean percentage within 20% 45.88%–46.35%). [3]
- In the White population, MARS and BART showed higher mean percentage within 20% and lower mean MAE than those of MLR. [3]
- When patients were grouped in terms of warfarin dose range, all machine learning techniques except ANN and LAR showed significantly higher mean percentage within 20%, and lower MAE than MLR in the lowand high. [3]
- genes have generally contributed to 6–18% and 15–30% of warfarin dose variability, respectively [7–12]. [3]
- Previous studies have developed predictive pharmacogenetic dosing algorithms for warfarin, and the results showed that the algorithms predicted 37–55% of the patient’s warfarin stable dose. [3]
- Performances of the algorithms were compared using two evolution indexes, namely, mean absolute error and the percentage of patients whose predicted warfarin dose was within 20% of the actual dose in the validating cohort. [3]
- We selected the percentage of patients within 20% of the actual dose (percentage within 20%). [3]
- The MAE and percentage within 20% of the algorithms were also compared in terms of race and warfarin dose range. [3]
- Warfarin dose range was divided into three categories based on the 25% and 75% quantiles of WSD by races low dose , intermediate dose , and high dose in the Asian population; low dose . [3]
- In the entire cohort, we randomly selected 80% among the eligible patients, as the “derivation cohort” to develop all dose. [3]
- The remaining 20% of the patients constituted the “validation cohort,” which was used to test the final selected algorithms. [3]
- The MAE and mean percentage within 20% in the whole population, as well as in terms of warfarin dose range were obtained after 100 rounds of resampling. [3]
- Furthermore, 95% confidence interval of MAE was calculated. [3]
- To test the differences of the mean percentage within 20% among these algorithms, two independent sample t. [3]
- To determine a correlation between the average MAE and mean percentage within 20%, Spearman’s correlation test was performed. [3]
- Among the patients, 83.64% were aged 50 years or older. [3]
- About 73.97% of the total population was homozygous for the CYP2C9*1 allele, whereas 4.17% comprised noncarriers of this wild. [3]
- A/A, A/G and G/G were 26.82%, 30.83% and 30.14%, respectively. [3]
- 84–9.82 mg/week) and mean percentage within 20% (41.27–46.35%). [3]
- Some machine learning based algorithms, including SVR, MARS and BART resulted in lower MAE and higher mean percentage within 20%. [3]
- (MAE ranged from 8.84 mg/week to 8.96 mg/week, mean percentage within 20% ranged from 45.88% to 46.35%) than those of all the other algorithms; t test results showed that all p values were <0.05. [3]
- By contrast, ANN performed the least feasible (average MAE was 9.82; mean percentage within 20% was 41.27%). [3]
- The average MAE was inversely correlated with the percentage within 20%. [3]
- Data are expressed as mean (95% CI). [3]
- Overall, the difference in the mean percentage within 20% of the algorithms across the three cohorts was much smaller than that in the average MAE. [3]
- All the algorithms yielded similar mean percentage within 20% across racial groups. [3]
- In the White population, BART, SVR, BRT, MARS and RFR, showed higher mean percentage within 20% and lower MAE than those of MLR. [3]
- In the Asian population, no significant difference existed in the MAE and mean percentage within 20% among SVR, BART, BAR, MARS and MLR, these five techniques also performed better than the other algorithms. [3]
- MARS and BART showed the lowest MAE and the highest mean percentage within 20% in the White and Asian populations. [3]
- In the intermediatedose range, all the algorithms showed mean percentages within 20% in at least 55% of the patients, but a maximum of only 23.79% and 38.94% in the lowand high. [3]
- In extremely low or high warfarin dose range, six machine learning algorithms, SVR, RT, RFR, BRT, MARS and BART performed better than MLR, with significantly lower MAE and higher mean percentage within 20%. [3]
- Compared with MLR, the mean percentage within 20% of these six machinelearning based algorithms increased by 1.52% to 6.62% and 2.63% to 6.37% in the lowand high dose ranges, respectively. [3]
- Specifically, the MAEs, after randomly splitting the data as 50% derivation and 50% validation cohort followed by a bootstrap of 200 iterations, were 5.92 and 6.23 mg/week for ANN and MLR respectively. [3]
- Our results indicated that the mean percentages within 20% of all the studied algorithms do not differ in terms of race, whereas the average MAEs do. [3]
- The greatest difference in the mean percentage within 20% was also observed between these two populations at about 4.97% only. [3]
- Our findings indicated that the nine algorithms exhibited a lower MAE and a higher mean percentage within 20% in the intermediatedose range than those in the high. [3]
- These VFs were resampled to determine mean sensitivity, distribution limits , and SD for different ‘x’ and numbers of resamples. [4]
- Using the resampled sensitivities, we determined the mean , 95th percentile and 5th percentile , and the standard deviation. [4]
- Outliers were identified and removed using a combination of robust nonlinear regression and outlier removal (with Q = 10%; GraphPad Prism 7; GraphPad Inc., La Jolla, CA). [4]
- We compared the number of outliers removed by Q = 0.1% (n = 14, 0.05%), 1% (n = 24, 0.09%), and 10% (n = 119, 0.46%). [4]
- As expected, a Q of 10% removed the greatest number of outliers at all locations; over 52 test locations, this equated to approximately 1.8 more values removed per location compared with the 1% level. [4]
- Central tendency results were similar, but the variance was reduced when using Q = 10%. [4]
- The Q = 10% condition removed points that there at least 3.3 SD away from the mean, equating to a P value of 0.05%, which is the lowest level of significance flagged on the HFA total deviation and pattern deviation maps. [4]
- Thus, in order to obtain data with the most likely outliers removed, we continue to report results using Q = 10%. [4]
- The 5th percentile of the retrospective normative cohort was used as the lower limit of normality,. [4]
- To assess this difference, we determined the number of defects found using different percentile cut off values for normality , that is, receiver operator characteristic curves. [4]
- Mean, 95th percentiles, 5th percentiles, and SD values for each location within the 24 2 VF are shown in. [4]
- percentile sensitivity values for the retrospective cohort when the complete data set was used and when outliers were removed for locations within the HF 24. [4]
- For the k = 100 condition, one way ANOVA showed no significant effect of x on the difference between ground truth and bootstrapped means , but showed a significant difference in the 95th percentile ,. [4]
- 5th percentile , and SD are plotted for each set size condition. [4]
- Across Adjacent Levels for the Retrospective Cohort and Prospective Cohort for the Bootstrapped 95th Percentile, 5th Percentile, and SD Parameters. [4]
- 5th percentile , and SD . [4]
- The differences in the 95th percentile value were borderline in terms of statistical significance. [4]
- Mean and SD, 95th and 5th percentile sensitivity values for the prospective cohort . [4]
- 95th and 5th percentile sensitivity values for the prospective cohort . [4]
- With the retrospective and the prospective cohorts, only a small proportion of the total data set was required to provide a similar estimate of mean and distribution limits approximately 40% and 60% for retrospective and prospective, respectively. [4]
- Forbothn=300andn=400,wefoundalevelofxthatwassimilartowhenn=500wasusedx=150for95thpercentile,x=150for5thpercentile,andx=60forSD. [4]
- Inclusion of the complete data set resulted in on average 0.51 (95% confidence interval 0.44–0.60). [4]
- When using the 5th percentile from the original retrospective data as the ‘ground truth’, smaller set sizes tended to overestimate the number of ‘events’, and underestimated their depth, corresponding to higher 5th percentile and lower mean values. [4]
- Each datum point represents the average across all glaucoma patients for each level of x, and the error bars indicate the 95% confidence interval. [4]
- As expected, the AUROC was slightly greater when using a smaller set size, x = 6, in comparison to the other conditions, as the resultant percentile cut off values were higher under conditions of low specificity. [4]
- ROC curves plotting sensitivity (%) as a function of 100 − specificity (%). [4]
- In the case of VF studies the 5th percentile is often used as the cut off for an ‘event’, but therein lies a problem in a normal cohort of 20 subjects, the 5th percentile represents only one individual’s result. [4]
- The addition of 20 subjects at a time would only add one more subject with which to define the 5th percentile. [4]
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Reference
- nature – https://www.nature.com/articles/srep06496.
- who – https://www.who.int/data/gho/data/themes/topics/health-workforce.
- oecd – https://data.oecd.org/.
- plos – https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0135784.
- arvojournals – https://tvst.arvojournals.org/article.aspx?articleid=2674284.
How Useful is Database Comparison
One of the key benefits of database comparison is that it provides users with a comprehensive view of the various options available to them. By comparing features such as scalability, performance, security, and cost across different database systems, users can gain a better understanding of the trade-offs involved in choosing one system over another. This allows them to make more informed decisions based on which features are most important for their specific use case.
Furthermore, database comparison can help to identify areas for improvement in existing database systems. By highlighting the strengths and weaknesses of different systems, users can pinpoint areas where innovation is needed to address shortcomings or capitalize on strengths. This can drive competition in the database market, pushing vendors to continuously improve their offerings in order to stay competitive.
Database comparison can also help to foster collaboration and knowledge sharing within the database community. By sharing their findings and experiences with different database systems, users can help to build a collective knowledge base that benefits the entire community. This can lead to the development of best practices, standards, and guidelines that help to ensure the success of database projects across the board.
In addition, database comparison can be a valuable tool for database administrators and developers looking to optimize the performance of their database systems. By testing different configurations, indexes, and query optimization techniques on different systems, they can identify the most efficient ways to store, retrieve, and manipulate data. This can lead to significant performance improvements and cost savings for organizations that rely on database systems for their day-to-day operations.
Database comparison can also be useful for organizations looking to migrate from one database system to another. By comparing the features and capabilities of different systems, they can evaluate the feasibility and potential benefits of switching to a new system. This can help them to assess the risks and challenges involved in the migration process, and plan accordingly to ensure a smooth transition.
Overall, database comparison is a useful and powerful tool for anyone working with database systems. Whether you are a database administrator, developer, decision-maker, or researcher, the ability to compare and evaluate different database systems can help you make better decisions, drive innovation, and improve the efficiency and effectiveness of your database projects. As the field of database technology continues to evolve, database comparison will remain an invaluable tool for those looking to stay ahead of the curve.
In Conclusion
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