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. 2015 Apr 23;3:e913.
doi: 10.7717/peerj.913. eCollection 2015.

An Experimental Search Strategy Retrieves More Precise Results Than PubMed and Google for Questions About Medical Interventions

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Free PMC article

An Experimental Search Strategy Retrieves More Precise Results Than PubMed and Google for Questions About Medical Interventions

Robert G Badgett et al. PeerJ. .
Free PMC article

Abstract

Objective. We compared the precision of a search strategy designed specifically to retrieve randomized controlled trials (RCTs) and systematic reviews of RCTs with search strategies designed for broader purposes. Methods. We designed an experimental search strategy that automatically revised searches up to five times by using increasingly restrictive queries as long at least 50 citations were retrieved. We compared the ability of the experimental and alternative strategies to retrieve studies relevant to 312 test questions. The primary outcome, search precision, was defined for each strategy as the proportion of relevant, high quality citations among the first 50 citations retrieved. Results. The experimental strategy had the highest median precision (5.5%; interquartile range [IQR]: 0%-12%) followed by the narrow strategy of the PubMed Clinical Queries (4.0%; IQR: 0%-10%). The experimental strategy found the most high quality citations (median 2; IQR: 0-6) and was the strategy most likely to find at least one high quality citation (73% of searches; 95% confidence interval 68%-78%). All comparisons were statistically significant. Conclusions. The experimental strategy performed the best in all outcomes although all strategies had low precision.

Keywords: Evidence-based medicine; Google; Information retrieval; PubMed.

Conflict of interest statement

Dr Badgett created SUMSearch, which is the basis for the experimental search strategies. Dr Badgett receives no compensation for this project or for SUMSearch.

Figures

Figure 1
Figure 1. Selection of questions.
Figure 2
Figure 2. Precision by number of interations used by the experimental search engine.

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References

    1. AAMC-HHMI Scientific Foundation for Future Physicians Committee . Scientific Foundations for Future Physicians. 2009.
    1. American College of Physicians 2014. ACP smart medicine. Philadelphia: American College of Physicians. Available at http://smartmedicine.acponline.org (accessed 5 May 2014)
    1. Anders ME, Evans DP. Comparison of PubMed and Google scholar literature searches. Respiratory Care. 2010;55(5):578–583. - PubMed
    1. Aphinyanaphongs Y, Tsamardinos I, Statnikov A, Hardin D, Aliferis CF. Text categorization models for high-quality article retrieval in internal medicine. Journal of the American Medical Informatics Association. 2005;12(2):207–216. doi: 10.1197/jamia.M1641. - DOI - PMC - PubMed
    1. Badgett RG. How to search for and evaluate medical evidence. Seminars in Medical Practice. 1999;2(3):8–14.

Grant support

The authors declare there was no funding for this work.

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