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    [18F]FDG-PET or PET/CT in the evaluation of pelvic and para-aortic lymph nodes in patients with locally advanced cervical cancer: A systematic review of the literature
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    Abstract:
    Imaging is essential in detecting lymph node metastases for radiotherapy treatment planning in locally advanced cervical cancer (LACC). There are not many data on the performance of [18F]FDG-PET(CT) in showing lymph node metastases in LACC. We pooled sensitivity and specificity of [18F]FDG-PET(CT) for detecting pelvic and/or para-aortic lymph node metastases in patients with LACC. Also, the positive and negative posttest probabilities at high and low levels of prevalence were determined.MEDLINE and EMBASE searches were performed and quality characteristics assessed. Logit-sensitivity and logit-specificity estimates with corresponding standard errors were calculated. Summary estimates of sensitivity and specificity with corresponding 95% confidence intervals (CIs) were calculated by anti-logit transformation. Positive and negative likelihood ratios (LRs) were calculated from the mean logit-sensitivity and mean logit-specificity and the corresponding standard errors. The posttest probabilities were determined by Bayesian approach.Twelve studies were included with a total of 778 patients aged 10-85 years. For pelvic nodes, summary estimates of sensitivity, specificity, LR+ and LR- were: 0.88 (95%CI: 0.40-0.99), 0.93 (95%CI: 0.85-0.97), 11.90 (95%CI: 5.32-26.62) and 0.13 (95%CI: 0.01-1.08). At the lowest prevalence of 0.15 the positive predictive value (PPV) and negative predictive value (NPV) were 0.68 and 0.98, at the highest prevalence of 0.65, 0.96 and 0.81. For the para-aortic nodes, the summary estimates of sensitivity, specificity LR+ and LR- were: 0.40 (95%CI: 0.18-0.66), 0.93 (95%CI: 0.91-0.95), 6.08 (95%CI: 2.90-12.78) and 0.64 (95%CI: 0.42-0.99), respectively. At the lowest prevalence of 0.17 the PPV and NPV were 0.55 and 0.88, at the highest prevalence of 0.50, 0.86 and 0.61.The PPV and NPV of [18F]FDG-PET(CT) showing lymph node metastases in patients with LACC improves with higher prevalence. Prevalence and predictive values should be taken into account when determining therapeutic strategies based on [18F]FDG-PET(CT).
    In this paper, we compare logistic regression and 2 other classification methods in predicting hypertension given the genotype information. We use logistic regression analysis in the first step to detect significant single-nucleotide polymorphisms (SNPs). In the second step, we use the significant SNPs with logistic regression, support vector machines (SVMs), and a newly developed permanental classification method for prediction purposes. We also detect rare variants and investigate their impact on prediction. Our results show that SVMs and permanental classification both outperform logistic regression, and they are comparable in predicting hypertension status.
    Logistic model tree
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    We performed meta-analyses to calculate the differences between control (non-stimulated) and EPS-stimulated cells for the biological indices having enough data for such an analysis. In cases of not reported values, we used WebPlotDigitizer (v4.5,2021) to extract the information from the given graphs.In cases where the number of studies was not identified, we assumed that they were conducted in triplicates and in cases of a range of number of studies, we used the mean. Since different methods and scales were utilized in the eligible studies, we used standardized mean differences (SMDs) instead of absolute mean differences to standardize our findings to uniform scale. Missing SDs were imputed using the average coefficient of variation from all complete cases.
    Meta-regression
    Systematic error
    In 1993 Landtblom et al (1) reported a case-referent study on multiple sclerosis and exposure to solvents, ionizing radiation, and animals. Data were obtained from 91 cases and 348 referents. Multiple logistic regression was used in the analysis of the data, and 14 predictors were included in the final model. standard errors appear to be large, as reflected in the wide confidence intervals (CI). For example, the estimated odds ratio for occupational exposure to cats or dogs among men was 18 (95% CI 1.3--265). Despite the wide confidence interval the authors concluded that The men had significantly elevated risks, determined from logistic odds ratios, for . . . occupational contact with dog or cats, . . . [p 399]. odds ratio for X-ray treatment was reported to be for the women, 0 for the men, and 0 when both men and women were included in the model. Obviously there is a problem with the model, but the authors interpret the results in the following way This study indicates that exposure to ionizing radiation might have an increased risk for multiple sclerosis, as observed both for patients treated with X rays and for radiological personnel [p 402]. In my opinion this example illustrates the danger of an uncritical use of the logistic regression analysis. computer nicely calculates regression coefficients even if the number of empty cells is large and provides an error message only when the maximum number of iterations is reached. presence of zero cells should be recognized before the multiple logistic regression is carried out. By collapsing categories and excluding predictors with low prevalence the problem with empty cells can be diminished.
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    This paper presents confidence intervals for adjusted proportions using logistic regression with weighted sum contrasts. The methods are applied to data from two studies, (1) imposex percentages among female gastropods at different locations in the Gulf of Thailand adjusted for different species, and (2) complication-based neonatal morbidity risk for births at a major hospital adjusted for demographic factors.
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