A Network Analysis of Game of Thrones Analyze the network of characters in Game of Thrones and how it changes over the course of the books. Case Studies. Mridul Seth Data science enthusiast. David Venturi Instructor at DataCamp. Risk and Returns: The Sharpe Ratio Use pandas to calculate and compare profitability and risk of different investments using the Sharpe Ratio.
Exploring the Kaggle Data Science Survey Discover the top tools Kaggle participants use for data science and machine learning. Exploring the Evolution of Linux Find out about the development of the Linux operating system by exploring its Git repository history. Markus Harrer Software Development Analyst.
Clinical Case Study
Lars Hulstaert Data Scientist at Microsoft. Predicting Credit Card Approvals Build a machine learning model to predict if a credit card application will get approved. Peter Bull Co-founder of DrivenData. Wrangling and Visualizing Musical Data Wrangle and visualize musical data to find common chords and compare the styles of different artists. Kris Shaffer Data scientist and instructional technology specialist. Generating Keywords for Google Ads Automatically generate keywords for a search engine marketing campaign using Python.
Data Manipulation. Name Game: Gender Prediction using Sound Analyze the gender distribution of children's book writers and use sound to match names to gender. Tufool Alnuaimi Academic entrepreneur with a focus on data science. Apply machine learning methods in Python to classify songs into genres. Predict Taxi Fares with Random Forests Use regression trees and random forests to find places where New York taxi drivers earn the most. Who won?
Who got snubbed? Visualizing Inequalities in Life Expectancy Compare life expectancy across countries and genders with ggplot2. Baker spends a long time listening to auscultating Caleb's heart. Where on the thoracic surface do you auscultate to the tricuspid, mitral bicuspid , pulmonary, and aortic valves? Where do you think would be the best place to auscultate Caleb's abnormal heart sound? Explain your answer. Caleb has abnormal heart sounds that tipped the doctor off to a problem.
- Related terms:;
- Clinical Case Study Crimes Of The Heart A Case On Cardiac Anatomy?
- Related Essays and Questions;
Name the normal sounds of the heart and indicate what causes these sounds. In relation to the normal heart sounds, when would you expect to hear the abnormal sound Dr. Baker heard? You would expect to hear a swooshing noise in between S1 and S2. The defect in Caleb's heart allows blood to mix between the two ventricular chambers.
Essay on my hobby drawing for class 5
Due to this defect would you expect the blood to move from left-to-right ventricle or right-to-left ventricle during systole? Left-to-right Based on your understanding of blood pressure and resistance in the heart and great vessels, explain your answer to question 3a.
When an echocardiogram is performed, the technician color-codes oxygenated blood red and deoxygenated blood blue. In a healthy baby, what color would the blood be within the right and left ventricles, respectively?
In Caleb's heart, what color would the blood be within the right and left ventricles, respectively? Caleb's heart allows oxygenated and deoxygenated blood to mix. Based on your knowledge of the heart and the great vessels, describe other anatomical abnormalities that cause the mixing of oxygenated and deoxygenated blood. The "largest time-matched mean difference between drug and placebo" is the largest of these differences at any time point. The term "baseline-adjusted" in ICH E14 implies that the baseline data are taken into account in the statistical analysis. Differences in baseline assessment between cross-over and parallel design studies are discussed in Question 4.
Please discuss the need for baseline measurements, and when needed, how they should be collected, for cross-over and parallel design thorough QT studies. Adjustment for baseline measurements is potentially useful for several purposes, including detection of carry-over effects, reducing the influence of inter-subject differences and accounting for diurnal effects such as those due to food. There is no single best approach for baseline adjustment, but all planned baseline computations should be prospectively defined in the clinical trial protocol.
Case Study Collection - Search Results - National Center for Case Study Teaching in Science
Two kinds of baseline are commonly used: "time-matched" baseline taken at exactly the same time-points on the day prior to the beginning of treatment as on the treatment day and "pre-dose" baseline taken shortly prior to dosing. The "pre-dose" baseline is used for adjustment for inter-subject differences but not for diurnal effects.
The choice of baseline is influenced by whether the study is parallel or cross-over. For a parallel-group study a time-matched baseline allows the detection of differences in diurnal patterns between subjects that would not be detected by a predose baseline. In a parallel study a "time-matched" baseline day, if performed, would ideally occur on the day before the start of the study.
The "pre-dose" baseline is therefore usually adequate for cross-over studies. Has this investigation yielded a reasonable approach to concentration-response modeling during drug development? How can assessment of the concentration-response relationship guide the interpretation of QTc data? Concentration-response analysis, in which all available data across all doses are used to characterize the potential for a drug to influence QTc, can serve as an alternative to the by-time-point analysis or intersection-union test as the primary basis for decisions to classify the risk of a drug.
In either case this result is an important component of the totality of evidence assessment of the risk of QT prolongation. The overall assessment of risk of QT prolongation includes nonclinical data, the time course of QT prolongation, the magnitude of QT prolongation, categorical analyses of outliers, and certain adverse events in patients that can signal potential proarrhythmic effects. There are many different types of models for the analysis of concentration-response data, including descriptive pharmacodynamic PD models e.
It is recognized that concentration-response analyses of the same data using models with different underlying assumptions can generate discordant results. Therefore, it is important that the modeling methods and assumptions, criteria for model selection, rationale for model components, and potential for pooling of data across studies be specified prior to analysis to limit bias. Prospective specification of model characteristics e. On occasion, the QT effect is not a direct function of plasma concentration.
For example, drugs that cause QT prolongation as a result of changes in protein synthesis or trafficking or drugs with accumulation into myocardial tissues might demonstrate hysteresis. Testing for model assumptions, hysteresis a plot of data by-time point and a hysteresis loop plot , and goodness of fit should be documented. Concentration-response analysis can be challenging when more than one molecular entity-multiple drugs or parent plus metabolites-contributes to the QTc effect.
Important considerations Concentration-response data need not come from a dedicated QT study, nor even a single study, but there are several new and important considerations. Decision-making Both the intersection-union test and the concentration-response analysis can estimate the maximum effect of a drug treatment on the QTc interval, but they are not used to test the same hypothesis.
As mentioned above, inspection of the time course of QT prolongation is important. However, hypothesis testing based on a by-time point analysis intersection-union test or point estimate and confidence intervals is inappropriate in studies designed for a concentration-response analysis, if not powered to assess the magnitude of QT prolongation for each time point. See E14, Section 2. Other uses In addition to serving as the basis for regulatory decision-making, concentration-response analysis has established its utility in several settings enumerated below. Providing insight into regimens not studied directly An understanding of the concentration-response relationship can help predict the QT effects of doses, dosing regimens, routes of administration, or formulations that were not studied directly.
Interpolation within the range of concentrations studied is more reliable than extrapolation above the range. Predicting QTc effects of intrinsic and extrinsic factors that affect PK Understanding the concentration-response relationship can help predict the effects of intrinsic e. In most cases alternative designs can be used that may affect power considerations, but do not compromise study interpretation. For example, multiple doses can be studied in a parallel design trial or can use patients with the disease for which the drug is intended rather than healthy volunteers.
Other useful supplementary data might include intensive ECG data acquisition in early phase single or multiple ascending dose studies, utilisation of concentration-response analysis, and evaluation of exposures that are greater than are anticipated with the intended marketed dose.
A single dose of a positive control is generally sufficient, even if it precedes the investigational drug treatment. When a thorough QTc study of usual or modified design is not feasible, the intensity of late phase ECG monitoring will be dependent upon the quality and extent of the non-clinical and clinical evaluation. In situations where it is not possible to study higher exposures than are anticipated with the intended marketed dose, more intensive ECG monitoring might be necessary during Phase 3 trials. When the non-clinical and early clinical data do not suggest clinically relevant QTc prolongation, intensive late stage monitoring might not be necessary.
Otherwise, monitoring could be conducted as if a thorough QT study had been positive to protect patients in later trials and to obtain information on the frequency of marked QTc prolongation in the patient population. The ICH E14 Guideline does not address the approach to QT measurement during drug development in the case of combination drug products. Is it recommended that measurement of QT prolongation be performed on drug combinations?
Are sponsors expected to conduct thorough QT studies as part of the development of large proteins and monoclonal antibodies? It is not the purpose of the thorough QT study to assess the risk of Torsade de Pointes TdP in the target population, but rather to determine whether further data are warranted to assess risk. A finding of QT prolongation above the regulatory threshold of interest a positive thorough QT study might call for further electrocardiographic follow up in late phase studies.
The extent of the follow up would be affected by the magnitude of the estimated prolongation at doses and concentrations at which this occurs.
- Clinical Case Study Crimes Of The Heart A Case Study On Cardiac Anatomy Answers!
- ch_20_Case Study_Answer_Key.
- essay on indian corruption.
- Owl apa essay sample.
- - The Washington Post;
- thesis statement builder for compare and contrast essay.
- Make essay better generator.
If prolongation is substantial at concentrations expected to occur in clinical studies, it is important to protect patients in later trials and to obtain further information on the frequency of marked QT prolongation.