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Contents

Contents. 1. Introduction. 2. Procedure. The Results. 3. Conclusion. 4. Contents. 1. Introduction. 2. Procedure. The Results. 3. Conclusion. 4. Background. The rapid growth of the e.Publications . Most web search engines provide knowledge from different areas.

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Contents

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Presentation Transcript

  1. Contents 1 Introduction 2 Procedure The Results 3 Conclusion 4

  2. Contents 1 Introduction 2 Procedure The Results 3 Conclusion 4

  3. Background • The rapid growth of the e.Publications. • Most web search engines provide knowledge from different areas. • e.g. Google.com • Some databases are focusing on specialized fields. • e.g. PubMed • Each search engine is ranking the results depending on pre-defined factors.

  4. The Problem • The variety of ranking and classifications might hinder the researchers’ work. • Researchers and doctors spend long time to read all articles to find strong evidence to support their work. • Beginner researchers’ decisions might be affected by relying on articles have low – quality with high – ranking or low-quality with large number of citations. • The need to study and evaluate the popular ranking systems to focus on the most important publications.

  5. Project Aim • Test the rank of 2,075 medical publications in four web ranking systems and a specialized citation index, which are: • Google PageRank. • Yahoo WebRank. • Bing ranking systems. • Alexa ranking systems. • PubMed citation index. • Compare the results with the Strength of Recommendation Taxonomy ( SORT ) methodology.

  6. The Strength of Recommendation Taxonomy (SORT) • The SORT classification has been designed to determine the strength of evidences in the medical publications. • SORT uses three criteria to classify the publications: • The strength of recommendation for the body of evidence. • A  consistent and high quality patient-oriented evidence. • B  inconsistent or limited quality patient-oriented evidence. • C  opinions, disease-oriented, consensus and usual practice as well as case studies for treatment, diagnosis, screening or prevention. • The quality of the individual studies. ( 1, 2 , 3 ). • The consistency of the publication. ( Consistent or inconsistent )

  7. Contents 1 Introduction 2 Procedure The Results 3 Conclusion 4

  8. Development Steps

  9. Step 1: Generating the PubMed Publications’ Links • PubMed Publications’ ID have been given. • Automatic links generating. • The link = http://www.ncbi.nlm.nih.gov/pubmed/ + Publication ID The Generated links The Given Data

  10. Steps 2-5: TestingWeb Ranking Systems • Automatic links testing framework have been developed. • Pre-developed script has been used. • AJAX technologies have been used to improve the test speed.

  11. Step 6: Testing PubMed Database • PubMed citation index does not provide web service to implement the test. • The PubMed publication pages have been studied to develop an automated method to fetch the number of citations.

  12. Step 6: Testing PubMed Database Example of PubMed Page

  13. Step 6: Testing PubMed Database • The pages have been copied and analysed automatically. • The system is able to process around 20 links per test. • Some articles do not have citations. Example of PubMed Citation Format

  14. Step 6: Testing PubMed Database Example of PubMed test results

  15. Contents 1 Introduction 2 Procedure The Results 3 Conclusion 4

  16. Google PageRank Results Number of Publications 404 372 224 206 182 176 145 130 122 65 32 22 17 3 14 9 0 2 Publication rank Google PageRank Results

  17. Google PageRank Results • The correlation between the SORT classification and Google PageRank has been calculated using Pearson, Spearman and Kendall Correlation Coefficients as the following: • Pearson Correlation = 0.10793 • Spearman Rank Correlation = 0.09971 • Kendall Correlation = 0.08813

  18. Yahoo, Bing and Alexa Results • Yahoo assigned all results to zero. • Bing assigned all results to zero. • Alexa assigned all results as same as the root page; which is: http://www.nih.gov/ . e.g. In 2 / November / 2011, the rank of http://www.nih.gov/ was 355.  all publications’ ranks = 355

  19. PubMed Results Number of Publications 577 393 269 131 130 116 83 81 44 44 40 31 32 17 11 14 6 7 4 7 10 8 10 4 5 2 2 3 2 6 2 2 2 Number of Citations Google PageRank Results

  20. PubMed Results • The correlation between the SORT classification and PubMed citation index has been calculated among Pearson, Spearman and Kendall Correlation Coefficients as the following: • Pearson Correlation = 0.07157 • Spearman Rank Correlation = 0.05207 • Kendall Correlation = 0.04267

  21. Results comparison

  22. Contents 1 Introduction 2 Procedure The Results 3 Conclusion 4

  23. Conclusion • This study attempted to test whether the web ranking systems and citation indexes can help to determine the strength of 2,075 clinical publications according to their SORT classifications. • Three generic measures have been used to calculate the correlations. • Neither publication’s rank in the four popular systems nor the number of citations in PubMed database are correlated to the Strength Of Recommendation Taxonomy.

  24. Recommendations • Studying other citation indexes such as Google Scholar, ScienceDirect, and Scopus. • Track the positions of the authors who rank the publications and putting the publication date into the consideration

  25. Thank You!

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