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The pulmonary embolism rule-out criteria (PERC) helps assess people in whom pulmonary embolism is suspected, but unlikely. Unlike the Wells score and Geneva score , which are clinical prediction rules intended to risk stratify people with suspected PE, the PERC rule is designed to rule out the risk of PE in people when the physician has already ...
Retrieved from "https://en.wikipedia.org/w/index.php?title=PERC_rule&oldid=750954796"This page was last edited on 22 November 2016, at 13:36 (UTC) (UTC)
Pulmonary Embolism Rule-out Criteria (PERC Rule), a clinical decision-making tool to aid in the diagnosis of chest pain and/or dyspnea; Tetrachloroethylene or perc, a chemical widely used for dry cleaning and metal-degreasing; People. Perc Bushby (1919–1975), Australian rules footballer; Perc Horne (1890–1990), Australian rugby league ...
PERC [7] renamed the PERC Reporting Code (2008 edition) to the PERC Reporting Standard with the 2013 revision. A subsequent revision to the PERC Reporting Standard was undertaken in 2017, and a further revision was published in late 2021. [1] A detailed account of the historical development of the PERC Reporting Standard can be found on the ...
The Nelson rules were first published in the October 1984 issue of the Journal of Quality Technology in an article by Lloyd S Nelson. [2] The rules are applied to a control chart on which the magnitude of some variable is plotted against time. The rules are based on the mean value and the standard deviation of the samples.
Following Oregon football's dramatic win over Ohio State, the NCAA announced a rules clarification involving 12 defenders on the field.
In statistics, the one in ten rule is a rule of thumb for how many predictor parameters can be estimated from data when doing regression analysis (in particular proportional hazards models in survival analysis and logistic regression) while keeping the risk of overfitting and finding spurious correlations low. The rule states that one ...
The rule can then be derived [2] either from the Poisson approximation to the binomial distribution, or from the formula (1−p) n for the probability of zero events in the binomial distribution. In the latter case, the edge of the confidence interval is given by Pr( X = 0) = 0.05 and hence (1− p ) n = .05 so n ln (1– p ) = ln .05 ≈ −2.996.