Sentence examples for terms with sensitivity from inspiring English sources

Individual search terms with sensitivity ≥ 10% and specificity ≥ 10% for a given purpose category were incorporated into the development of search strategies that included 2 or more terms.

Individual terms with sensitivity > 25% and specificity > 75% for a given purpose category were incorporated into the development of search strategies that included a combination of 2 or more terms.

Thus, for MEDLINE and EMBASE, only individual search terms with sensitivity > 25% and specificity > 75% for a given purpose category were incorporated into the development of search strategies that included 2 or more terms.

Individual search terms with sensitivity > 25% and specificity > 75% for causation studies were incorporated into the development of search strategies that included a combination of 2 or more terms.

Search terms with high sensitivity and low specificity were chosen to produce a comprehensive list of search results.

Because the use of searchable technical terms for indexing international literature in databases is not always up-to-date, we defined a search strategy composed of free text terms, synonyms and MeSH terms with high sensitivity but low specificity.

In addition, it is important to consider equity measures over the long-term and with sensitivity to the "inverse equity hypothesis" associated with the introduction of new technologies [ 72], since evidence shows new technologies tend to be adopted by the wealthy and, therefore, are likely to increase health outcome inequities in the short-term.

Next, we tested all 2-term search strategies with sensitivity at least 75% and specificity at least 50% to find multiple term strategies that were optimized for sensitivity.

For the development of multiple-term search strategies to either optimize sensitivity or specificity, we tested all 2-term search strategies with sensitivity at least 75% and specificity at least 50%.

A population set consisting of a random sample of PubMed/Medline references was used to compare the frequency of terms with the highest sensitivity from the development set with the frequency in the overall PubMed/Medline population.

The negative binomial cusum algorithm showed greater congruence with epidemiological opinion in terms of sensitivity, with the EARS algorithms having significantly lower AUC scores than the negative binomial cusum.