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The book is designed according to the pathological classification of the benign and malignant ovarian neoplasm by presenting for each pathology the clinical setting followed by the imaging approach.
These percentages were calculated for each pathology (Table 4), showing that some pathologies had a high rate of exclusion (e.g., pneumonia/TB, splenomegaly, uterine myomatosis, ovarian cyst, cirrhosis, gastritis/PUD).
However, modeling of such complex signals brings along its own challenges and requires significant amount of data for each pathology, a requirement that is hard to meet in the currently available datasets.
For each pathology, therapeutic options are discussed and, when applicable, drug dosages are proposed.
Mean ± SEM of the proposed parameters, for each pathology and level of pathology.
Sensitivity, specificity, accuracy, PPV, and NPV values are illustrated in bar graphs for each pathology SoT in Figure 3.
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Specificities and sensitivities were also determined for differentiating each pathology type from the other four using a multivariate analysis.
Consultations involving chronic disease were also associated with ordering lesser numbers of pathology tests (OR 0.99 for each extra pathology test).> We found that 29.5% of trainee consultations included a chronic disease diagnosis or problem and that the most commonly encountered chronic diseases were hypertension, depressive disorder, lipid disorder, asthma, and oesophageal disease.
The combined scores were plotted for each BPH pathology category (minimal, mild, moderate, severe BPH changes), and Kruskal-Wallis tests were used to compare the combined scores across different disease severity status.
The algorithm was applied repeatedly for each existing pathology.
It has also been suggested that for each heart pathology there is a specific miRNA signature.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com