AIM： To use the cumulative sum analysis score（CUSUM） to construct objectively the learning curve of phacoemulsification competency.METHODS： Three second-year residents and an experienced consultant were monitored ...AIM： To use the cumulative sum analysis score（CUSUM） to construct objectively the learning curve of phacoemulsification competency.METHODS： Three second-year residents and an experienced consultant were monitored for a series of 70 phacoemulsification cases each and had their series analysed by CUSUM regarding posterior capsule rupture（PCR） and best-corrected visual acuity. The acceptable rate for PCR was 〈5%（lower limit h） and the unacceptable rate was 〉10%（upper limit h）. The acceptable rate for bestcorrected visual acuity worse than 20/40 was 〈10%（lower limit h） and the unacceptable rate was 〉20%（upper limit h）. The area between lower limit h and upper limit h is called the decision interval. RESULTS： There was no statistically significant difference in the mean age, sex or cataract grades between groups. The first trainee achieved PCR CUSUM competency at his 22 nd case. His best-corrected visual acuity CUSUM was in the decision interval from his third case and stayed there until the end, never reaching competency. The second trainee achieved PCR CUSUM competency at his 39^ th case. He could reach best-corrected visual acuity CUSUM competency at his 22 ^nd case. The third trainee achieved PCR CUSUM competency at his 41 st case. He reached bestcorrected visual acuity CUSUM competency at his 14 ^th case.CONCLUSION： The learning curve of competency in phacoemulsification is constructed by CUSUM and in average took 38 cases for each trainee to achieve it.展开更多
The traditional method for creating a gene score to predict a given outcome is to use the most statistically significant single nucleotide polymorphisms (SNPs) from all SNPs which were tested. There are several disadv...The traditional method for creating a gene score to predict a given outcome is to use the most statistically significant single nucleotide polymorphisms (SNPs) from all SNPs which were tested. There are several disadvantages of this approach such as excluding SNPs that do not have strong single effects when tested on their own but do have strong joint effects when tested together with other SNPs. The interpretation of results from the traditional gene score may lack biological insight since the functional unit of interest is often the gene, not the single SNP. In this paper we present a new gene scoring method, which overcomes these problems as it generates a gene score for each gene, and the total gene score for all the genes available. First, we calculate a gene score for each gene and second, we test the association between this gene score and the outcome of interest (i.e. trait). Only the gene scores which are significantly associated with the outcome after multiple testing correction for the number of gene tests (not SNPs) are considered in the total gene score calculation. This method controls false positive results caused by multiple tests within genes and between genes separately, and has the advantage of identifying multi-locus genetic effects, compared with the Bonferroni correction, false discovery rate (FDR), and permutation tests for all SNPs. Another main feature of this method is that we select the SNPs, which have different effects within a gene by using adjustment in multiple regressions and then combine the information from the selected SNPs within a gene to create a gene score. A simulation study has been conducted to evaluate finite sample performance of the proposed method.展开更多
Currently, mere are many onune review weo sites where consumers can freely write comments about different kinds of products and services. These comments are quite useful for other potential consumers. However, the num...Currently, mere are many onune review weo sites where consumers can freely write comments about different kinds of products and services. These comments are quite useful for other potential consumers. However, the number of online comments is often large and the number continues to grow as more and more consumers contribute. In addition, one comment may mention more than one product and con- tain opinions about different products, mentioning something good and something bad. However, they share only a single overall score, Therefore, it is not easy to know the quality of an individual product from these comments. This paper presents a novel approach to generate review summaries including scores and description snippets with re- spect to each individual product. From the large number of comments, we first extract the context （snippet） that includes a description of the products and choose those snippets that express consumer opinions on them. We then propose several methods to predict the rating （from 1 to 5 stars） of the snip- pets. Finally, we derive a generic framework for generating summaries from the snippets. We design a new snippet selec- tion algorithm to ensure that the returned results preserve the opinion-aspect statistical properties and attribute-aspect cov- erage based on a standard seat allocation algorithm. Through experiments we demonstrate empirically that our methods are effective. We also quantitatively evaluate each step of our ap- proach.展开更多
文摘AIM： To use the cumulative sum analysis score（CUSUM） to construct objectively the learning curve of phacoemulsification competency.METHODS： Three second-year residents and an experienced consultant were monitored for a series of 70 phacoemulsification cases each and had their series analysed by CUSUM regarding posterior capsule rupture（PCR） and best-corrected visual acuity. The acceptable rate for PCR was 〈5%（lower limit h） and the unacceptable rate was 〉10%（upper limit h）. The acceptable rate for bestcorrected visual acuity worse than 20/40 was 〈10%（lower limit h） and the unacceptable rate was 〉20%（upper limit h）. The area between lower limit h and upper limit h is called the decision interval. RESULTS： There was no statistically significant difference in the mean age, sex or cataract grades between groups. The first trainee achieved PCR CUSUM competency at his 22 nd case. His best-corrected visual acuity CUSUM was in the decision interval from his third case and stayed there until the end, never reaching competency. The second trainee achieved PCR CUSUM competency at his 39^ th case. He could reach best-corrected visual acuity CUSUM competency at his 22 ^nd case. The third trainee achieved PCR CUSUM competency at his 41 st case. He reached bestcorrected visual acuity CUSUM competency at his 14 ^th case.CONCLUSION： The learning curve of competency in phacoemulsification is constructed by CUSUM and in average took 38 cases for each trainee to achieve it.
文摘The traditional method for creating a gene score to predict a given outcome is to use the most statistically significant single nucleotide polymorphisms (SNPs) from all SNPs which were tested. There are several disadvantages of this approach such as excluding SNPs that do not have strong single effects when tested on their own but do have strong joint effects when tested together with other SNPs. The interpretation of results from the traditional gene score may lack biological insight since the functional unit of interest is often the gene, not the single SNP. In this paper we present a new gene scoring method, which overcomes these problems as it generates a gene score for each gene, and the total gene score for all the genes available. First, we calculate a gene score for each gene and second, we test the association between this gene score and the outcome of interest (i.e. trait). Only the gene scores which are significantly associated with the outcome after multiple testing correction for the number of gene tests (not SNPs) are considered in the total gene score calculation. This method controls false positive results caused by multiple tests within genes and between genes separately, and has the advantage of identifying multi-locus genetic effects, compared with the Bonferroni correction, false discovery rate (FDR), and permutation tests for all SNPs. Another main feature of this method is that we select the SNPs, which have different effects within a gene by using adjustment in multiple regressions and then combine the information from the selected SNPs within a gene to create a gene score. A simulation study has been conducted to evaluate finite sample performance of the proposed method.
基金This work was partially supported by the National Science Foundation of China （Grant Nos. 61103039, 61232002, 61472345）, National Basic Research Program of China （2010CB731402） and Wuhan Key Lab Research Foundation （SKLSE2012-09-16）.
文摘Currently, mere are many onune review weo sites where consumers can freely write comments about different kinds of products and services. These comments are quite useful for other potential consumers. However, the number of online comments is often large and the number continues to grow as more and more consumers contribute. In addition, one comment may mention more than one product and con- tain opinions about different products, mentioning something good and something bad. However, they share only a single overall score, Therefore, it is not easy to know the quality of an individual product from these comments. This paper presents a novel approach to generate review summaries including scores and description snippets with re- spect to each individual product. From the large number of comments, we first extract the context （snippet） that includes a description of the products and choose those snippets that express consumer opinions on them. We then propose several methods to predict the rating （from 1 to 5 stars） of the snip- pets. Finally, we derive a generic framework for generating summaries from the snippets. We design a new snippet selec- tion algorithm to ensure that the returned results preserve the opinion-aspect statistical properties and attribute-aspect cov- erage based on a standard seat allocation algorithm. Through experiments we demonstrate empirically that our methods are effective. We also quantitatively evaluate each step of our ap- proach.
