J. Med. Internet Res. - Development and validation of filters for the retrieval of studies of clinical examination from Medline.

Tópicos

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{ case(1353) use(1143) diagnosi(1136) }
{ learn(2355) train(1041) set(1003) }
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{ method(2212) result(1239) propos(1039) }

Resumo

CKGROUND: Efficiently finding clinical examination studies--studies that quantify the value of symptoms and signs in the diagnosis of disease-is becoming increasingly difficult. Filters developed to retrieve studies of diagnosis from Medline lack specificity because they also retrieve large numbers of studies on the diagnostic value of imaging and laboratory tests.OBJECTIVE: The objective was to develop filters for retrieving clinical examination studies from Medline.METHODS: We developed filters in a training dataset and validated them in a testing database. We created the training database by hand searching 161 journals (n = 52,636 studies). We evaluated the recall and precision of 65 candidate single-term filters in identifying studies that reported the sensitivity and specificity of symptoms or signs in the training database. To identify best combinations of these search terms, we used recursive partitioning. The best-performing filters in the training database as well as 13 previously developed filters were evaluated in a testing database (n = 431,120 studies). We also examined the impact of examining reference lists of included articles on recall.RESULTS: In the training database, the single-term filters with the highest recall (95%) and the highest precision (8.4%) were diagnosis[subheading] and "medical history taking"[MeSH], respectively. The multiple-term filter developed using recursive partitioning (the RP filter) had a recall of 100% and a precision of 89% in the training database. In the testing database, the Haynes-2004-Sensitive filter (recall 98%, precision 0.13%) and the RP filter (recall 89%, precision 0.52%) showed the best performance. The recall of these two filters increased to 99% and 94% respectively with review of the reference lists of the included articles.CONCLUSIONS: Recursive partitioning appears to be a useful method of developing search filters. The empirical search filters proposed here can assist in the retrieval of clinical examination studies from Medline; however, because of the low precision of the search strategies, retrieving relevant studies remains challenging. Improving precision may require systematic changes in the tagging of articles by the National Library of Medicine.

Resumo Limpo

ckground effici find clinic examin studiesstudi quantifi valu symptom sign diagnosi diseasei becom increas difficult filter develop retriev studi diagnosi medlin lack specif also retriev larg number studi diagnost valu imag laboratori testsobject object develop filter retriev clinic examin studi medlinemethod develop filter train dataset valid test databas creat train databas hand search journal n studi evalu recal precis candid singleterm filter identifi studi report sensit specif symptom sign train databas identifi best combin search term use recurs partit bestperform filter train databas well previous develop filter evalu test databas n studi also examin impact examin refer list includ articl recallresult train databas singleterm filter highest recal highest precis diagnosissubhead medic histori takingmesh respect multipleterm filter develop use recurs partit rp filter recal precis train databas test databas haynessensit filter recal precis rp filter recal precis show best perform recal two filter increas respect review refer list includ articlesconclus recurs partit appear use method develop search filter empir search filter propos can assist retriev clinic examin studi medlin howev low precis search strategi retriev relev studi remain challeng improv precis may requir systemat chang tag articl nation librari medicin

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