![]() Carbapenem-Susceptible OXA-23-Producing Proteus mirabilis in the French Community. Potron A, Hocquet D, Triponney P, Plésiat P, Bertrand X, Valot B. mirabilis isolates, with the ability to form a biofilm that carries integron, extended-spectrum -lactamases (ESBLs), and plasmid-mediated colistin resistance genes (mcrThus, the spread of multidrug-resistant P. An evaluation of multidrug-resistant (MDR) bacteria in patients with urinary stone disease: data from a high-volume stone management center. Proteus mirabilis is a biofilm-forming agent that quickly settles on the urinary catheters and causing catheter-associated urinary tract infections.![]() High prevalence of CTX-M-1 group in ESBL-producing enterobacteriaceae infection in intensive care units in southern Chile. Pavez M, Troncoso C, Osses I, Salazar R, Illesca V, Reydet P, Rodríguez C, Chahin C, Concha C, Barrientos L. Antibiotic Susceptibility Patterns and Prevalence of Some Extended Spectrum Beta-Lactamases Genes in Gram-Negative Bacteria Isolated from Patients Infected with Urinary Tract Infections in Al-Najaf City, Iraq. In vitro efficacy of phytotherapeutics suggested for prevention and therapy of urinary tract infections. Marcon J, Schubert S, Stief CG, Magistro G.
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![]() This is something that you have to take into account when reporting your findings, but it cannot be measured using Fleiss' kappa. It is also worth noting that even if raters strongly agree, this does not mean that their decision is correct (e.g., the doctors could be misdiagnosing the patients, perhaps prescribing antibiotics too often when it is not necessary). For example, the individual kappas could show that the doctors were in greater agreement when the decision was to "prescribe" or "not prescribe", but in much less agreement when the decision was to "follow-up". Furthermore, an analysis of the individual kappas can highlight any differences in the level of agreement between the four non-unique doctors for each category of the nominal response variable. Since the results showed a very good strength of agreement between the four non-unique doctors, the head of the large medical practice feels somewhat confident that doctors are prescribing antibiotics to patients in a similar manner. The level of agreement between the four non-unique doctors for each patient is analysed using Fleiss' kappa. The 10 patients were also randomly selected from the population of patients at the large medical practice (i.e., the "population" of patients at the large medical practice refers to all patients at the large medical practice). ![]() This process was repeated for 10 patients, where on each occasion, four doctors were randomly selected from all doctors at the large medical practice to examine one of the 10 patients. The four randomly selected doctors had to decide whether to "prescribe antibiotics", "request the patient come in for a follow-up appointment" or "not prescribe antibiotics" (i.e., where "prescribe", "follow-up" and "not prescribe" are three categories of the nominal response variable, antibiotics prescription decision). Therefore, four doctors were randomly selected from the population of all doctors at the large medical practice to examine a patient complaining of an illness that might require antibiotics (i.e., the "four randomly selected doctors" are the non-unique raters and the "patients" are the targets being assessed). ![]() We explain these three concepts – random selection of targets, random selection of raters and non-unique raters – as well as the use of Fleiss' kappa in the example below.Īs an example of how Fleiss' kappa can be used, imagine that the head of a large medical practice wants to determine whether doctors at the practice agree on when to prescribe a patient antibiotics. ![]() In addition, Fleiss' kappa is used when: (a) the targets being rated (e.g., patients in a medical practice, learners taking a driving test, customers in a shopping mall/centre, burgers in a fast food chain, boxes delivered by a delivery company, chocolate bars from an assembly line) are randomly selected from the population of interest rather than being specifically chosen and (b) the raters who assess these targets are non-unique and are randomly selected from a larger population of raters. Fleiss' kappa in SPSS Statistics Introductionįleiss' kappa, κ (Fleiss, 1971 Fleiss et al., 2003), is a measure of inter-rater agreement used to determine the level of agreement between two or more raters (also known as "judges" or "observers") when the method of assessment, known as the response variable, is measured on a categorical scale. |
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