915 resultados para Subject headings


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How to search CINAHL using their thesaurus

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Mimeographed.

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Commonly referred to as: The red book.

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Print ceases at v.48 (2007), then becomes online only.

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Mode of access: Internet.

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Mode of access: Internet.

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Mode of access: Internet.

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PURPOSE: To assess the Medical Subject Headings (MeSH) indexing of articles that employed time-to-event analyses to report outcomes of dental treatment in patients.

MATERIALS AND METHODS: Articles published in 2008 in 50 dental journals with the highest impact factors were hand searched to identify articles reporting dental treatment outcomes over time in human subjects with time-to-event statistics (included, n = 95), without time-to-event statistics (active controls, n = 91), and all other articles (passive controls, n = 6,769). The search was systematic (kappa 0.92 for screening, 0.86 for eligibility). Outcome-, statistic- and time-related MeSH were identified, and differences in allocation between groups were analyzed with chi-square and Fischer exact statistics.

RESULTS: The most frequently allocated MeSH for included and active control articles were "dental restoration failure" (77% and 52%, respectively) and "treatment outcome" (54% and 48%, respectively). Outcome MeSH was similar between these groups (86% and 77%, respectively) and significantly greater than passive controls (10%, P < .001). Significantly more statistical MeSH were allocated to the included articles than to the active or passive controls (67%, 15%, and 1%, respectively, P < .001). Sixty-nine included articles specifically used Kaplan-Meier or life table analyses, but only 42% (n = 29) were indexed as such. Significantly more time-related MeSH were allocated to the included than the active controls (92% and 79%, respectively, P = .02), or to the passive controls (22%, P < .001).

CONCLUSIONS: MeSH allocation within MEDLINE to time-to-event dental articles was inaccurate and inconsistent. Statistical MeSH were omitted from 30% of the included articles and incorrectly allocated to 15% of active controls. Such errors adversely impact search accuracy.

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The following paper deals with an automatic text classification method which does not require training documents. For this method the German Subject Heading Authority File (SWD), provided by the linked data service of the German National Library is used. Recently the SWD was enriched with notations of the Dewey Decimal Classification (DDC). In consequence it became possible to utilize the subject headings as textual representations for the notations of the DDC. Basically, we we derive the classification of a text from the classification of the words in the text given by the thesaurus. The method was tested by classifying 3826 OAI-Records from 7 different repositories. Mean reciprocal rank and recall were chosen as evaluation measure. Direct comparison to a machine learning method has shown that this method is definitely competitive. Thus we can conclude that the enriched version of the SWD provides high quality information with a broad coverage for classification of German scientific articles.

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Second edition.