When writing a manuscript, we often use words such as perfect, strong, good or weak to name the strength of the relationship between variables. However, it is unclear where a good relationship turns into a strong one. The same strength of r is named differently by several researchers. Therefore, there is an absolute necessity to explicitly report the strength and direction of r while reporting correlation coefficients in manuscripts. This article aims to familiarize medical readers with several different correlation coefficients reported in medical manuscripts, clarify confounding aspects and summarize the naming practices for the strength of correlation coefficients.

IntroductionThe objective of this study was to determine whether bedside visual estimates of left ventricular systolic function (LVSF) by emergency physicians (EP) would agree with quantitative measurement of LVSF by the modified Simpson’s method (MSM), as recommended by the American Society of Echocardiography.MethodsAfter limited focused training, 2 trained EPs performed bedside echocardiography (BECH) procedures s between January and June 2012 to prospectively evaluate patients presenting to the emergency department (ED) with dyspnea. EPs categorized their visually estimated ejection fractions (VEF) as either low or normal. Formal echocardiography were ordered and performed by an experienced cardiologist using the MSM and accepted as the criterion standard. We compared BECH results for each EP using chi-squared testing and performed correlation analysis by Pearson correlation coefficient.ResultsOf the 146 enrolled patients with dyspnea, 13 were excluded and 133 were included in the study. Comparison of EPs vs. cardiologist’s estimate of ejection fraction yielded a Pearson’s correlation coefficient of 0.77 (R, p<0.0001) and 0.78 (R, p<0.0001). Calculated biserial correlations using point-biserial correlation and z-scores were 1 (rb, p<0.0001) for both EPs. The agreement between EPs and the cardiologist was 0.861 and 0.876, respectively. The sensitivity, specificity, positive predictive value, negative predictive value, and the positive and negative likelihood ratios for each physician were 98.7–98.7%, 86.2–87.9%, 0.902–0.914, 0.980–0.981, 7.153–8.175, 0.015–0.015, respectively.ConclusionEPs with a focused training in limited BECH can assess LVSF accurately in the ED by visual estimation.

BackgroundWe wished to compare the San Francisco Syncope Rule (SFSR), Evaluation of Guidelines in Syncope Study (EGSYS) and the Osservatorio Epidemiologico sulla Sincope nel Lazio (OESIL) risk scores and to assess their efficacy in recognising patients with syncope at high risk for short-term adverse events (death, the need for major therapeutic procedures, and early readmission to the hospital). We also wanted to test those variables to designate a local risk score, the Anatolian Syncope Rule (ASR).MethodsThis prospective, cohort study was conducted at the emergency department of a tertiary care centre. Between December 1 2009 and December 31 2010, we prospectively collected data on patients of ages 18 and over who presented to the emergency department with syncope.ResultsWe enrolled 231 patients to the study. A univariate analysis found 23 variables that predicted syncope with adverse events. Dyspnoea, orthostatic hypotension, precipitating cause of syncope, age over 58 years, congestive heart failure, and electrocardiogram abnormality (termed DO-PACE) were found to predict short-term serious outcomes by logistic regression analysis and these were used to compose the ASR. The sensitivity of ASR, OESIL, EGSYS and SFSR for mortality were 100% (0.66 to 1.00); 90% (0.54 to 0.99), 80% (0.44 to 0.97) and 100% (0.66 to 1.00), respectively. The specificity of ASR, OESIL, EGSYS and SFSR for mortality were 78% (0.72 to 0.83); 76% (0.70 to 0.82); 80% (0.74 to 0.85) and 70% (0.63 to 0.76). The sensitivity of ASR, OESIL, EGSYS and SFSR for any adverse event were 97% (0.85 to 1.00); 70% (0.52 to 0.82); 56% (0.40 to 0.72) and 87% (0.72 to 0.95). The specificity of ASR, OESIL, EGSYS and SFSR for any adverse event were 72% (0.64 to 0.78); 82% (0.76 to 0.87); 84% (0.78 to 0.89); 78% (0.71 to 0.83), respectively.ConclusionThe newly proposed ASR appears to be highly sensitive for identifying patients at risk for short-term serious outcomes, with scores at least as good as those provided by existing diagnostic rules, and it is easier to perform at the bedside within the Turkish population. If prospectively validated, it may offer a tool to aid physicians' decision-making.

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BACKGROUND: Cardiac arrests in hospital areas are common, and hospitals have rapid response teams or "blue code teams" to reduce preventable in-hospital deaths. Education about the rapid response team has been provided in all hospitals in Turkey, but true "blue code" activation is rare, and it is abused by medical personnel in practice. This study aimed to determine the cases of wrong blue codes and reasons of misuse. METHODS: This retrospective study analyzed the blue code reports issued by our hospital between January 1 and June 1 2012. A total of 89 "blue code" activations were recorded in 5 months. A "blue code" was defi ned as any patient with an unexpected cardiac or respiratory arrest requiring resuscitation and activation of a hospital alert. Adherence to this defi nition, each physician classifi ed their collected activation forms as either a true or a wrong code. Then, patient data entered a database (Microsoft Excel 2007 software) which was pooled for analysis. The data were analyzed by using frequencies and the Chi-square test on SPSSv16.0. RESULTS: The patients were diagnosed with cardiopulmonary arrest (8), change in mental status (18), presyncope (11), chest pain (12), conversive disorder (18), and worry of the staff for the patient (22). Code activation was done by physicians in 76% of the patients; the most common reason for blue code was concern of staff for the patient. CONCLUSION: The fi ndings of this study show that more research is needed to establish the overall effectiveness and optimal implementation of blue code teams.