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Optimized search strategy for detecting scientifically strong studies on treatment through PubMed

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Abstract

Our study was designed to optimize the search strategies based on the work of Haynes et al. for detecting randomized controlled trials (RCTs) through PubMed. In particular, we aimed to improve precision for broad and narrow searches on interventional studies. We used in addition to the string suggested by the Hedge Team the following: {NOT ((animals [mh] NOT humans [mh]) OR (review [pt] OR meta-analysis [pt]))} and tested its effectiveness. The search was carried out on a year’s worth of articles from the PubMed database. We analyzed 35,590 bibliographic citations about four relevant major topics in internal medicine (hypertension, diabetes, heart failure, and hepatitis). Precision, percentage gain between the Hedge Team search strategies and the new one were computed and reported in the text. Moreover, a pooled analysis was carried out in terms of absolute precision difference. We observed better precision for both broad and narrow searches. However, effective gain resulted only for broad searches. In this case, bibliographic citation recall effectively reduced (−24 to −35 % retrieved citation with a gain of 32–54 %) without loss of information. The search strategy improved broad searches regarding each of the four considered topics. We think this new search strategy, based on a previous work of the Hedge team, could be a step forward and can save some time by researchers.

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Notes

  1. search term indicates one of the four topics considered (hypertension, diabetes, heart failure, and hepatitis). For each of them broad and narrow searches were performed.

Abbreviations

dp:

Publication date

pt:

Publication type

mh:

MeSH terms

sh:

MeSH subheading

tiab:

Title/abstract

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Conflict of interest

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Authors and Affiliations

  1. Centre of Research for Effectiveness and Appropriateness in Medicine (C.R.E.A.M), Dipartimento Biomedico di Medicina Interna e Specialistica (DiBiMIS), University of Palermo, Piazza delle Cliniche 2, 90127, Palermo, Italy

    Salvatore Corrao, Daniela Colomba, Luigi Calvo & Giuseppe Licata

  2. Laboratory of Clinical Epidemiology, Biostatistics and Knowledge Management, Dipartimento Biomedico di Medicina Interna e Specialistica (DiBiMIS), Università di Palermo, Piazza delle Cliniche 2, 90127, Palermo, Italy

    Rosario Scaglione

  3. Divisione di Medicina Interna, Fondazione San Raffaele-G.Giglio, Cefalù, Palermo, Italy

    Christiano Argano

Authors
  1. Salvatore Corrao
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  2. Daniela Colomba
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  3. Christiano Argano
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  4. Luigi Calvo
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  5. Rosario Scaglione
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  6. Giuseppe Licata
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Corresponding author

Correspondence to Salvatore Corrao.

Appendix—search strategies

Appendix—search strategies

Narrow search

# 1:“search term”Footnote 1 AND 2006 [dp].

# 2: (randomized controlled trial [pt] OR (randomized [tiab] AND controlled [tiab] AND trial [tiab])).

# 3: # 1 AND # 2.

# 4: # 3 NOT ((animals [mh] NOT humans [mh]) OR (review [pt] OR meta-analysis [pt])).

Broad search

# 1:“search term” AND 2006 [dp].

# 2: ((clinical [tiab] AND trial [tiab]) OR clinical trials [mh] OR clinical trial [pt] OR random*[tiab] OR random allocation [mh] OR therapeutic use [sh]).

# 3: # 1 AND # 2.

# 4: # 3 NOT ((animals [mh] NOT humans [mh]) OR (review [pt] OR meta-analysis [pt])).

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Corrao, S., Colomba, D., Argano, C. et al. Optimized search strategy for detecting scientifically strong studies on treatment through PubMed. Intern Emerg Med 7, 283–287 (2012). https://doi.org/10.1007/s11739-012-0773-1

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  • DOI: https://doi.org/10.1007/s11739-012-0773-1

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