Michaël D. M. HenrotteGhent University | UGhent
Michaël D. M. Henrotte
MB ChB
About
4
Publications
8,288
Reads
How we measure 'reads'
A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more
12
Citations
Citations since 2017
Introduction
Skills and Expertise
Additional affiliations
September 2017 - June 2020
Position
- Graduate Teaching Assistant in physiology
Description
- Teaching practical implementations of cardiac, respiratory, and neurophysiology, at the faculty (under)graduate courses.
July 2017 - present
Position
- Graduate Teaching Assistant in anatomy
Description
- Assistance in the practical training of osteology, gross (topographical) anatomy and in vivo surface anatomy of the locomotor system and its associated vascular and neurological structures at the faculty (under)graduate courses.
September 2015 - July 2017
Position
- Undergraduate Teaching Assistant in anatomy
Description
- Assistance in the practical training of osteology, gross (topographical) anatomy and in vivo surface anatomy of the locomotor system and its associated vascular and neurological structures at the faculty undergraduate courses.
Education
September 2017 - June 2020
September 2014 - July 2017
Publications
Publications (4)
Introduction
Gliomas are the most frequent malignant primary brain tumours in adults and can be classified into low-grade gliomas (LGG, WHO grade I-II) and high-grade gliomas (HGG, WHO grade III-IV). Overall survival rate of HGG is poor (months) when compared to LGG (years). Accurate and early diagnosis, for which histopathology is the gold standar...
Background
Gliomas are the most frequent malignant primary brain tumours in adults, accounting for about 70% of adult malignant primary brain tumours. They can be categorised into low-grade gliomas (LGG, WHO grade I-II) or high-grade gliomas (HGG, WHO grade III-IV). Accurate and early diagnosis of these tumours, for which histopathological analysis...
OBJECTIVE
The effect of CSF on blood coagulation is not known. Enhanced coagulation by CSF may be an issue in thrombotic complications of ventriculoatrial and ventriculosinus shunts. This study aimed to assess the effect of CSF on coagulation and its potential effect on thrombotic events affecting ventriculovenous shunts.
METHODS
Two complementary...
Questions
Questions (2)
Hello
When testing for normality of value distribution of a certain variable, wanting to compare the values between two or more distinct groups (e.g., men vs. women), does it suffice to assess normality of the distribution in the whole sample, or is it necessary to assess normality of the distribution of the values among men, and among women, separately?
Thanks.
Dear members
I would like to ask anyone with a good knowledge on confounding variables and IBM SPSS Statistics (23.0 or above) for Windows, how, in a sample with lots of input and output variables, the output variable predictions (test results) by the input variables of interest may be adjusted for other input variables that may very well confound with the test result.
Consider the following situation:
You have 95 primary input variables (input variables of interest)
- These 95 variables are all continuous variables.
You have 5 primary outcome variables (output variables of interest)
- 2 are continuous
- 2 are categorical with 2 categories
- 1 is categorical with more than 2 categories
You have identified 17 additional input variables, of which each one may or may not influence (interfere with any of the) primary (both input and output) variables.
- 4 are continuous
- 9 are categorical with 2 categories
- 4 are categorical with more than 2 categories
The research question is simple: determine the role the 95 primary input variables have in predicting the 5 primary outcome variables.
Potential problem: possible interference by any of the 17 "additional" input variables. Your ultimate goal is to get a test result that has been adjusted for the variables that have shown to significantly influence either input or output variables.
Let's set an example: you wish to assess the role (any of the) 95 variables (has) have in predicting one of the 2 continuous primary outcome variables. This may be done with Pearson's correlation coefficient, or with Spearman's rank correlation coefficient. The choice depends on the satisfaction of parametric testing prerequisites (most importantly: distribution of variable values statistically insignificantly differing from a Gaussian distribution; and homoscedasticity across the variable values).
You want to adjust the results of either Pearson or Spearman's test in SPSS for all of the 17 possibly confounding variables, that SPSS might identify as a significant confounder. How can this be achieved (easily) in SPSS?
Thank you very much in advance.
