What’s up ? This is THE stephane ANDRE. With my training, I’m interested in biomechanics to avoid injuries. I read « Sport Medicine Media Guide » and I learned some good stuff.
Shoulder is the most mobile joint of the body. This allows you to lift your arm, rotate your arm and lift your arm over your head. It’s possible to have a greater range of motion with less stability.
This happens when the humerus head (the upper arm bone) is forced out of the shoulder’s cavity. Usually this happens as a result of a sudden traumatic injury.
Once the shoulder is dislocated, the shoulder is vulnerable to repeat. When the shoulder is loose and slips several times, it’s called a chronic shoulder instability.
The shoulder is made of 3 bones : humerus (upper arm bone), scapula (shoulder blade) and clavicle (collarbone).
This may be partial, which means that the arm’s ball partially comes out from the cavity. This is called a subluxation. This can be complete which means that the arm’s ball comes out completely from the cavity.
Symptoms of chronic shoulder instability are :
Pain caused by the shoulder injury
Repeated shoulder’s dislocation
Repeated instance of the shoulder giving out
A persistent sensation of the shoulder that is loose, slipping out of the joint or hanging.
Specific tests help assess shoulder instability (including general relaxation of ligaments). A doctor may prescribe imaging tests such as X-rays, CT Scan or MRI to confirm the diagnosis and identify other problems.
First, chronic shoulder instability treated with nonsurgical options. If these options don’t relieve pain and instability, surgery may be needed.
Generally, it often takes several months of nonsurgical treatment before success can be assessed. Nonsurgical treatments includes :
Non-steroidal anti-inflammatory medication
Often, surgery is often required to repair torn or stretched ligaments so that they can maintain the shoulder joint in place.
Bankart lesions (tearing of the front labrum from the cavity) can be repaired surgically using suture anchors to reattach the ligaments to the bone.
Arthroscopy => Soft tissues of the shoulder can be repaired using tiny instruments and small incisions. It’s a procedure that is done the same day or outpatient. Arthroscopy is a minimally invasive surgery. The surgeon examines the inside of the shoulder with a small camera and performs the operation with special instruments.
Open surgery => These are patients who require open surgical intervention. This involves making a wider incision on the shoulder and performing the repair under direct visualization.
After surgery, the shoulder can be temporarily immobilized with a sling. When the sling is removed, it’s essential to do ligament rehabilitation exercises. These exercises improve the range of motion of the shoulder and avoid scarring during ligament healing. Thereafter, exercises for strengthening the shoulder will be added in the rehabilitation program.
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What’s up ? This is THE stephane ANDRE. With my training, I’m interessted in biomechanics to avoid injuries. I read « Sport Medicine Media Guide » and I learned some good stuff.
Articular cartilage is difficult to understand because there are 3 types of cartilages in the body : articular of hyaline cartilage (covers joint surfaces), fibrocartilage (knee meniscus, vertebral disk) and elastic cartilage (outer ear). These cartilage’s types differ in their structure, elasticity and strength.
Articular cartilage is a complex element, it’s a living tissue that is on the joint’s surface. The function is to provide a low friction surface to allow the joint to withstand weight loads through the range of motion needed to perform activity of daily living. To put it simply, articular cartilage is a very thin shock absorber. It’s built in 5 distinct layers and each layer has a structural and biochemical difference.
Articular cartilage injury may be due to trauma or progressive degeneration (wear and tear). This can be mechanical destruction, a direct blow or other trauma. The healing of articular cartilage cells depends on the severity of the damage and the location of the lesion. Articular cartilage has no direct blood supply so it has very little ability to repair itself. It the lesion penetrates the bone under the cartilage, the bone provides blood in the area which improves the chances of healing.
Mechanical degeneration (wear and tear) of articular cartilage occurs with progressive loss of normal cartilage structure and function. This loss begins with the softening of the cartilage, then progresses to fragmentation. As the loss of articular cartilage lining continue, the underlying bone no longer has any protections against normal wear and tear of daily life and begins to get damaged leading to osteoarthritis.
In many cases, a patient experiences knee swelling and vague pain. At this stage, continuous physical activity isn’t possible. If a loose body is present, words such as « locking » or « catching » might be used to explain the problem. With wear and tear , the patient often experiences stiffness, decreased range of motion, joint pain and/or swelling.
The physician examines the knee to look for a decrease in range of motion, pain along the joint line, swelling, fluid on the knee, abnormal alignment of the joint’s bones, and ligament or meniscal injury.
Cartilage lesions are difficult to diagnose and it’s possible that the use of magnetic resonance imaging (MRI) or arthroscopy may be necessary. Plain X- rays don’t usually diagnose articular cartilage problems but they used to rule out other abnormalities.
Articular cartilage injury that doesn’t penetrate the bone doesn’t repair itself. A lesion that penetrates the bone can heal but the type of cartilage created is structurally unorganized and doesn’t work as well as the original cartilage.
Lesion less than 2 cm have the best prognosis and the best treatment options. These options are arthroscopic surgery using techniques to remove damaged cartilage and increase blood flow from the underlying bone (drilling, pick procedure or microfracture ).
For smaller lesion of articular cartilage surgery is not required.
For larger lesion, it’s necessary to transplant the articular cartilage from another area of the body. Talk to your doctor or specialist to have more information about the decision to have a surgical operation.
For patients with osteoarthritis, non-surgical treatment consists of physical therapy, lifestyle modification (for example reducing activity), bracing, supportive devices, oral and injection drugs (like non-steroidal inflammatory drugs, cartilage protective drugs) and medical management.
Surgical options depend on the severity of osteoarthritis and may provide a reduction in symptoms that are usually short-lived. Total osteoarthritis may relieve the symptom of advanced osteoarthritis but this usually requires a change in the lifestyle and/or level of activity of the patient.
Based on published studies, the overall prevalence of articular cartilage injury in the knee is 36% among all athlete and 59% among asymptomatic basketball players and runners.
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I have just enrolled in a Data Science course on Udemy and I learned good stuff.
We’ll move to the next level. We’ll work with 2 bar charts in parallel to have a more efficient data mining. In a previous article, we created 2 different bar charts. The 1st was an A/B test (actually, it’s a classification test) that told us in which age range the clients were most likely to leave the bank. The 2nd was a bar chart showing the age distribution of clients in our sample of 10 000 clients.
Let’s go. We’re going to have an A/B test with age range and we’ll add a bar chart of the client distribution below. To add a bar chart, we must start by choosing what we want to keep and what we want to add. In our case, we want to keep the columns because they’re the same in the 2 bar charts.
And we just want to add a new line so we will add a new variable in « Rows ». As we want to add a bar chart of distribution, we will use the variable which corresponds to the number of observation « Number of Records ».
In « Measures » moves the variable « Number of Records » in « Rows » to the right of « SUM(Number of Records).
We have a 2nd bar chart below the 1st bar chart. As you can see, these 2 bar charts are in one column. « Columns » is « Age(bins) ». These 2 bar charts are in 2 different lines which are the lines that correspond to the 2 « SUM(Number of Records) » in « Rows ».
The space on the left has also changed. There is « All » which represents the 2 bar charts at the same time. It means, when your select « All », you make change in the 2 bar charts.
Below this tab « All » we have 2 tabs. The 1st tab represents the 1st bar chart so the 1st « SUM(Number of Records) » in « Rows » and the 2nd tab represents the 2nd bar chart so the 2nd « SUM(Number of Records) » in « Rows ».
Which means that if you want to make changes on the 2 bar charts at the same time, you make the changes in the tab « All ». If you want to make changes only in the first bar chart, you select the first tab below « All ». If you want to make changes only in the 2nd bar chart, you select the second tab below « All ».
So if you change the color in tab « All », our 2 bar charts will be colored by the same color.
Select the « All » tab and click on « Colors ».
Click on « Edit Colors… » and select « Stayed ». Select the green color and click on the « OK » button.
As you can see, the color changed in the 2 bar charts.
Click on the tab of the 2nd bar chart.
Removes the « Exited » variable from « Colors » to remove colors only in the 2nd bar chart.
Removes the « SUM(Number of Records) » variable from « Label » to remove the labels only in the 2nd bar chart.
We will add color on this 2nd bar chart. Click on « Colors », click on « More colors… » and select the blue color. Click on the « OK » button.
Now, we would like to see the colors vary in intensity depending on the number of observations. Take « SUM(Number of Records) » from the 2nd line in « Rows » and holding « Ctrl » or « Command », move it to « Colors ».
Cool ! We will take care of the 1st bar chart. Select the tab of the 1st bar chart.
Click on « Colors ». Click on « Edit Colors… ». Select « Stayed ». Select the brown color and click on the « OK » button.
For more clarity, we will add labels in 2nd bar chart. Click on the tab of the 2nd bar chart. Take « SUM(Number of Records) » from « Colors » and holding « Ctrl » or « Command » and move it to « Labels ».
Perfect. Now we will change the location of the bar chart. We will put the 2nd bar chart instead of the 1st bar chart. According to the logic of « Rows » and « Columns », simply put the 2nd line « SUM(Number of Records) » to the left to pass in 1st line.
BOOM, the bar chart of the age distribution is going over because it’s in the 1st line in « Rows ». With these changes, tabs to change the bar charts have changed order.
What we can observe with these bar chart is that we see on the 1st bar chart that the majority of bank’s clients are in the age group of 30 to 34 years old and 35 to 39 years old. In these 2 age groups, we see on the 2nd bar chart that client of 30 to 34 years old are less likely to leave the bank than clients between 35 and 39 years old. Look at ages 30 to 34, the rate of clients leaving the bank is 8% while in the 35 to 39 age group, the number of clients leaving the bank is 13%.
In the age group of 40 to 54 years old, we see on the 2nd bar chart that the rate of clients leaving the bank is increasing and is above of the average rate of clients leaving the bank (20%). But we see in the 1st bar chart that the number of clients in the age group of 40 to 54 years old decrease with the age groups.
Do you remember the potential for anomalies in age groups 75, 85 and 90 ? We’ll check it. In the 1st bar chart we can see that there are 11 clients in the age group of 80 to 84 years old, 2 clients in the age group of 85 to 89 years old and 2 clients in the age group of 90 to 94 years old. We can conclude that these observations in age group of 80, 85 and 90 aren’t very significant from a statistical point of view because 2 clients is something negligible in this sample of 10 000 clients.
In the first age group of 15 to 19 years old, we can see that there are 49 clients, which is not very significant.
Compare these 2 bar chart in parallel allows us to have additional insights.
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I have just enrolled in a Data Science course on Udemy and I learned good stuff.
It’s cool, you finished the 1st part. Now we’re going to do more deep Data Mining analysis with this bank’s dataset.
To make these analyzes more deep, we’ll create a more statistical approach.
To do that we will create a new tab.
For this new tab, we want to understand how client distributed according to their age. Is there a majority of young or old people ?
Move the variable « Age » in « Columns ».
As we want to see the distribution of client ages, we need to use the variable « Number of Records » to see the number of observations. Move the variable « Number of Record » to « Rows ».
Boom, we have a chart but there is only one point on the top right. What happened is that Tableau took the sum of the ages of all the bank’s clients and the sum of all the « Number of Records », it means the total number of clients, 10 000 clients.
We’ll find a solution but before we’ll change the format to better see the chart. Right-click in the middle of the chart and select « Format ».
For the font’s size, select « 12 ».
Here you can see that the total age is 39 218 but that’s not what we’re looking for. What we want to see is the number of clients for each age.
I’ll explain what’s going on. We took the aggregated sums of our variables. Aggregate means that we took the total sum of the variable for each category. We added the ages but in fact we want to see the total number of observations for each age separately.
To have that, just click on the arrow in « SUM(Age) » in « Columns ».
Then select « Dimensions »
You see, Tableau doesn’t take the aggregated sum of ages but it takes ages separately. We have a curve that shows us the continuous distribution of our clients ages. That is to say, for each age, the curve gives is the number of clients of this age.
We’ll look at the dataset. Right-click on « Churn Modelling » and select « View Data… ».
There is window that appears that shows us the data in detail. If you scroll to the right, you will find the column « Age ».
We see that the ages rounded. As all ages rounded, Tableau is able to group clients by age. By positioning the mouse on the curve, we can see that there are 200 clients who are 26 years old.
If in the dataset, ages weren’t rounded, you would have seen clients with 26.5 or 26.3 years. It would create a lot of irregularity, there would be plenty of spikes with lots of variations.
Oooooh look, there is a variation that isn’t normal.
Let’s analyze it in detail. Around this peak, we see that there are 348 clients who are 29 years old.
Here, 404 clients who are 31 years old.
And this peak down that shows us that there are 327 clients who are 30 years old.
How to explain this irregularity ? It’s possible that many people of 29 years old are about to turn 30 years old and many people of 31 years old who just had 31 years old. It’s chance that make us have inaccuracies. You may have other inaccuracies if you data isn’t precise and rounded. In our case, the ages are rounded but we want to get rid of our small irregularity that we see on our curve.
There is way to see our distribution without our irregularities, it’s « bins ». « Bins » consists of grouping the information into different categories. That is we’re going to regroup our clients in different age groups.
Right-click on « Age » in « Measures ». Select « Create » and select « Bins… ».
A window appears. We’ll group our clients in 5-years increments. In « Size of bins », write « 5 » and click on the « OK » button.
As you can see, the variable « Age » has remained in « Measures » but there is a new variable in « Dimensions ».This is the variable we created « Age(bins) ».
Our « Age(bins) » variable was correctly placed in « Dimensions » because it is a category variable because each category corresponds to a 5-year age group.
For example, one category is 20 to 24 age group. Now we’ll create a new distribution based on « bins ».
To do that, we’ll remove the variable « Age » from « Columns » with a click and drag outside.
You move the variable « Age(bins) » from « Dimensions » to « Columns ».
In this case, it’s not possible to directly replace « Age » by « Age(bins) » over « Age » on « Columns ». This is because « Age » is a measure and « Age(bins) is a dimension.
That’s nice distribution, it’s usually the type of distribution (chart) we see in economics or mathematics. The difference with the old chart is that this chart is discrete. This chart is discrete because the clients grouped by age group while the previous chart was continuous.
On this distribution (chart), each bar corresponds to an age range. For example, this bar corresponds to the 25-29 age group.
Now, we’ll change the colors.
In « Row », move « SUM(Number of Record) » while holding down the « Ctrl » or « Command » key on your keyboard to « Colors ».
We get our distribution in blue but we’ll change the color to red. Click on « Colors » and click on « Edit Colors »
In the window that appears, click on the blue square on the right to display the color pallet.
Select the red color and click on the « OK » button.
Click on the « OK » button of the « Edit Colors » window.
To facilitate the reading of the bar chart, we’ll add the number of clients in each age group. In « Row », move « SUM (Number of Record) » while holding the « Ctrl » or « Command » key on your keyboard to « Label ».
That’s it, we can see how many clients there are in each age group.
We see that the dominant bar is the 35-39 age bracket and the second dominant bar is the 30-34 age bracket. Overall, we can see that most clients are between 25 and 40 years old, which seems consistent.
On our bar chart, we have absolute values. We’ll replace that with percentages. Click in the little arrow in « SUM(Number of Records) » in « Label » and you select « Add Table Calculation… » but I’ll show you another way to do it.
Instead of clicking « Add Table Calculation… », click on « Quick Table Calculation » and select « Percent of total ».
It’s cool, we have the exact percentage of people in each age bracket. Now, we can see that in the 25 to 40 age group, we have 20 + 23 +17= 60% of clients.
I’ll show you one last thing.You can change the size of the slices easily, just click on « Age(bins) » and select « Edit ».
In the windows, you can change the size of the slices (bins). Put « 10 » instead of « 5 » to get 10-years slices. Click on the « OK » button.
Now, we have a distibution with fewer slices and the dominant slice is 30 to 39 years old.
Well, it was just to show you how to change the size of bins. To go back to the old distribution with the 5-years slices, click on « Back » button.
As you can see, the values on bars are in percentages but the values on the axis are in absolutes values. Here is an exercise that I ask you to do : « Put the values of the axis in percentage ». I’ll give you the answer the next article.
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Hook you feet in the gym ladder with your thighs vertically. Your back to the floor and your hands behind your head :
Inhale and raise your torso as high as possible by rounding your spine.
Exhale at the end of the movement.
This exercise mainly works rectus abdominis and a little bit internal and external obliques.
It’s good to know that farther is your torso from the gym ladder and hook your feet lower, this increases the mobility of the pelvis. This allows a greater range of motion and works more flexors muscles of the hips (iliopsoas, rectus femoris and tensor fasciae latae).
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