Lori has a Bachelor of Science Degree in Physical Education, with an emphasis in Athletic Training, from San Diego State University. Lori also has worked at the Arco Olympic Training Center in Chula Vista in the sports medicine department. Certified Athletic Trainers (ATCs) are specialists in the prevention, evaluation, and rehabilitation of athletic injuries and are recognized by the American Medical Association as Allied Health Care Providers. The San Diego City College Athletic Medicine Program has internship positions open for anyone interested in becoming a Student Athletic Training Intern. If you are a new or returning San Diego City College student-athlete, you are required to fill out the forms linked below. The athletic training room is designed to allow athletic trainers to evaluate students who may have suffered an injury or have a general question about sports medicine.
Athletic Training is the prevention, evaluation, treatment, management and rehabilitation of athletic related injuries and illnesses by an AMA (American Medical Association) certified health care professional. Judicious use of evidence can help us become more efficient and effective by guiding our selection of diagnostic tools, determining appropriate interventions, and ascertaining if a treatment is working or not. An understanding of these concepts, coupled with their appropriate application based on the specific patient population, is essential. Before we begin discussing the various aspects of the diagnostic process, it is important to have a solid understanding of the key terminology. An underlying requirement for establishing a testa€™s clinical usefulness is that it has acceptable reliability. Prevalence, the extent to which a condition is present in a specific population, is influential to many of the concepts discussed later in this section. Knowing how likely an event is, or the concept of probability, is another important feature of understanding the statistical foundations of EBP.
The pre-test probability of a patient having the condition of interest is initially identified as the prevalence, which is particular to the population. For example, each of the grey dots in the box below represents the population of patients with knee pain, some of whom will have a diagnosis of an ACL sprain. A test with 100% accuracy will correctly classify the patients as either having an ACL sprain (blue dots) or not having an ACL sprain each time (green dots), as depicted by the below picture. While ita€™s tempting to think that we can correctly categorize patients 100% of the time using a test or cluster of tests, the reality is that this is impossible.
A reference standard represents the most accurate tool available for assessing a current condition or determining an outcome.
In a well-designed research study, subjects with similar profiles (eg, all having knee pain) would have been assessed by one or more examiners using standardized procedures, including the test of interest.
A As an example, leta€™s examine a hypothetical new test for detecting ACL sprains, the Declan test. In other words, 78% of the time, the Declan test correctly classified individuals as either having or not having ACL sprains. A quick glance at the results tells us that the Declan test is probably better than chance at identifying those with ACL sprains, but what else do we know about the usefulness of the test?
Sensitivity, sometimes called the true positive rate, is the testa€™s ability to detect those patients who actually have the condition as compared to the reference standard. The higher the sensitivity, the more likely that a Negative finding rules out the disorder. In other words, with a high sensitivity the test is so successful at identifying who has the disorder that anyone left over definitely does not. As alluded to previously, a common problem with tests that have high sensitivity, however, is that they falsely identify some as having the condition when, in reality, they do not. Specificity, also known as the true negative rate, is the testa€™s ability to detect those who do not have the condition as compared to the reference standard. In other words, with a high specificity the test is so successful at identifying those who do not have the disorder that we can rule in the condition for anyone left over.
In the case of cervical spine fracture, the Canadian C-Spine Rule has demonstrated high sensitivity and acceptable specificity in identifying clinically important cervical spine injuries. For more information on the Canadian C-Spine Rule, please refer to the following article:Steill IG, Clement CM, McKnight RD, Brison R, Schull MJ, Rowe BH, et al.
The Declan test correctly identified 17 people (TP) as having ACL sprains who actually had ACL sprains but did not identify the remaining 6 people (FN) who actually have ACL sprains as confirmed by arthroscopy. In a perfect world, diagnostic tests would have both a high sensitivity and a high specificity, which is easily interpreted as, a€?the test is useful to rule in and rule out a diagnosis.a€? However, this outcome is rarely the case. In the next module, predictive values, likelihood ratios, and receiver operating characteristic curves will be explained.
In the previous module, prevalence, accuracy, sensitivity, and specificity were introduced.
Predictive values and likelihood ratios help us to further interpret the 2 x 2 contingency table and interpret the combined influence of sensitivity and specificity.
Positive Likelihood Ratio: estimate of how much a positive finding changes the probability that a patient has the condition.
Negative Likelihood Ratio: estimate of how much a negative finding changes the probability that a patient has the condition.
Examining the effectiveness of the Declan test, our hypothetical example, in detecting ACL sprains, a sensitivity of 74% and a specificity of 82% are revealed. From these predictive values, we can determine that a positive Declan test will be accurate 85% of the time.
Predictive values are heavily influenced by the prevalence of a condition; these values may be misinterpreted, especially when comparing different populations in different studies. In a group where the prevalence of the target condition is very low, almost every patient will have a negative finding already. If another group is examined in which the prevalence of the target condition is higher, the number of false positives will be lower because more people actually have the condition.
Consider again the overall population in the United States versus the population of women who come to an orthopedic surgeona€™s office with complaints of acute knee injury. Predictive values must be considered in the context of the prevalence of the condition in the population under study. While predictive values may be helpful when studying similar populations, we still need a way to interpret a 2 x 2 contingency table to figure out whether or not the test is convincing in its ability to change the likelihood that a condition is or is not present. LRs combine the influence of sensitivity and specificity to indicate how much more or less confident we are that the patient has the condition after we conduct a test or series of tests.
The positive likelihood ratio (LR+) represents the change in our confidence that the condition is present based on a positive test. Basically, we are about 4 times more confident in a diagnosis of an ACL sprain after a positive finding with the Declan test. Conversely, the negative likelihood ratio (LR-) represents the change in our confidence that the condition is present given a negative test result. A negative finding of 0.32 is equivalent to finding a negative test in 32 of 100 individuals that have the condition. The likelihood (or post-test probability) is so high that the condition exists, that further testing is unnecessary. We should include other, possibly better, tests to more inform ourA decision making because the shift in probability is inconclusive. The likelihood (post-test probability) is so low that the conditionA exists that we are confident about ruling out the diagnosis. While we would like most of our tests to fall into the first or third category the reality is that most are somewhere in the middle. If the pre-test probability (prevalence) of a condition is known for the group being studied, a nomogram can be used to estimate the shift in post-test probability.
The pre-test probability and the likelihood ratios are plotted on the nomogram and then a line is drawn through those two dots to intersect with the post-test probability line.

For the Declan test, leta€™s assume that wea€™re studying patients who go to an orthopedic surgeona€™s office because of acute knee pain and that the pre-test probability (or prevalence) of ACL sprains for this group is 20%.
A positive finding on the Declan test increases the probability that the patient has the condition by about 30% (from 20% pre-test probability to 50% post-test probability). Based upon the prevalence, we have increased our confidence by 30% that the Declan test has identified that the patient does in fact have an ACL injury. In the previous module, we discussed the importance of using techniques that influence our clinical decision making in a known manner. The Clarke sign, conventionally used to diagnose chondromalacia and other patellofemoral syndromes, was recently investigated. What does your instinct and clinical experience tell you about the worthiness of this test? Remember, this is the number of positives detected by the test compared to the number of positives detected by the gold standard. This is the number of negatives detected by the test compared to the number of negatives detected by the gold standard. The positive predictive value reflects how often a positive finding with the test is correct, or the relationship of the true positives to the false positives. The positive likelihood ratio represents the extent to which we are more confident that the condition exists based on a positive test and is influenced by both sensitivity and specificity. The negative likelihood ratio represents our confidence that the condition exists based on a negative test and is also influenced by specificity and sensitivity. The bottom line is that the negative and positive likelihood ratios are both near one, telling us that the likelihood of the condition (or post-test probability) is not changed much after results of the Clarke sign are considered.
This module will discuss seven measures frequently used in epidemiology and often seen in research articles. To understand injury rate and injury risk, you must first understand the differences between prevalence and incidence. The risk of sustaining a grade II anterior talofibular ankle sprain for soccer players in this high school conference is 25%. Injury rate (incidence rate) is described as the number of injuries per unit of participant exposure.
One varsity high school soccer team, consisting of 22 players, has a total of 50 practices and 14 games during the fall season. Injury rate tells us that approximately 6 grade II anterior talofibular ankle sprains will occur per 1,000 participant exposures within a fall soccer season.
Injury risk provides us with the number of newly injured individuals within a specified time period. During one winter season, an athletic trainer collected injury data for one basketball team including 12 players. This value suggests that any athlete on this team has a 58.33% chance of sustaining at least one injury during one season. Now that we have discussed injury risk and injury rate, leta€™s move on to some other frequently used epidemiological measures. Relative risk is a calculation that allows us to consider the injury risks or injury rates between two groups. A Please note that either the injury risk of two groups or the injury rate of two groups can be used to determine the remaining epidemiological measures discussed in this module. A You would like to assess the effectiveness that a jump landing training protocol has on the prevention of ACL injuries in female soccer players.
She started her career at City College, filling in for the athletic trainer, who went on maternity leave. She had the opportunity to deal with athletes on all levels, from developmental to elite status.
For instance, if we knew that the use of a particular intervention would increase the chances of a successful outcome to a large extent in patients with a certain condition, then we would use that intervention. Likewise, the condition under which the diagnostic test was assessed is not consistent with how the test might be used by athletic trainers. Often, especially in the orthopedic literature, a diagnostic test is evaluated for accuracy before its reliability is established and these results should be interpreted with caution.
A condition that is prevalent in 1% of the population will be identified by random chance in 1 out of every 100 people.
The post-test probability describes the extent to which we are more or less certain that the patient has the condition following interpretation of the test results. To tell how good a test is in terms of classifying patients, the results are compared against those of a reference (or gold) standard. But, as will be described later, accuracy is heavily influenced by the prevalence of a condition, so we still dona€™t know much about the usefulness of the test.
In this case, we desire a test with a high sensitivity, because ita€™s critical to identify every person who might have the condition so that the appropriate stabilization and intervention can occur. As shown in the above example, all of the green dots with negative findings are identified correctly as not having a cervical spine fracture.
In other words, false positives (represented by green dots in the Positive Test square below) are picked up along the way. Opposite to sensitivity, it answers the question, a€?What proportion of people who do not have the condition are correctly identified by the test.a€? A 100% specific test, where the green dots represent individuals without the condition and the blue dots represent individuals with the condition, is depicted in the figure below. Many athletic trainers would opt to refer anyone with a hint of suspicion of significant cervical spine injury to an emergent care facility. The results of assessing how well the Declan test performed against the reference standard of arthroscopy are below. Sensitivity (again, the testa€™s ability to detect those patients who actually had the condition as compared to the reference standard) is calculated by dividing the number of positives correctly identified by the test (or true positives) by the total number of positives identified by the reference standard (true positives + false negatives).
These concepts help us interpret sensitivity and specificity and help answer the question of clinical usefulness.
While helpful in providing information regarding how well a test rules in or rules out a condition, we still need information about how to interpret these values. In other words, a negative likelihood ratio indicates the likelihood that a negative test result was obtained in a person with the condition as compared to a person without the condition. This leads to an inflated negative predictive value (the number of those correctly identified as negative will be naturally high).
In the first group (overall population), the prevalence of ACL injury will be quite low, and it will be easy to identify who does not have an ACL injury (resulting in a high negative predictive value).
As stated before, prevalence values are routinely difficult to come by and vary greatly by population. In other words, is the post-test probability (probability that a condition exists after performing a diagnostic test) that a patient does or does not have the condition higher than the pre-test probability (probability that a condition exists before a diagnostic test is performed) that a patient does or does not have a condition?
Therefore, making the other features of evidence-based practice a€“ the expertise of the clinician and the needs of the patient a€“ even more critical. In other words, how likely is a positive test result if the condition is present compared to the likelihood of a positive test result if the condition is not present?
In other words, how likely is it the patient has the condition in the presence of a negative test? All in all a€“ especially given the high positive likelihood ratio associated with the Lachman test a€“ the Declan test should probably not be added to our collection of diagnostic tools for ACL injuries.
A negative finding on the Declan test decreases the probability that the patient has the condition by 13% (from 20% pre-test probability to about 7% post-test probability).
However, there is still has a 7% chance that the Declan test will not identify the ACL injury. In this experiment, the results of the Clarke sign (positive or negative) were compared to whether or not arthroscopy revealed chondromalacia (Dobertstein, Romeyn, & Reineke, 2008).
Leta€™s check your intuition against statistical measures introduced in this module and the Accuracy, Sensitivity and Specificity Module.

The negative predictive value reflects how often a negative finding with the test is correct, or the relationship of the true negatives to the false negatives. Until other research proves otherwise, this test does not add much to the diagnostic process and its use should be discontinued.
It began as the study of epidemics, focusing on the morbidity and mortality of infectious diseases. When calculating prevalence, time is important and the same time period should be referenced for the numerator and the denominator.
While time is arbitrary, it is important in these calculations and should clearly identified.
Participant exposure is defined as one individual participating in one unit of exposure during which the individual is exposed to the possibility of an injury. Assuming that all 22 players participated in every practice and game, we can conclude that the team had 1,408 participant exposures (64 exposures x 22 players). However, it does not take into account the number of injuries one person may sustain during the time period.
By the end of the season, the team totaled 1,286 participant exposures (practices and games) and reported 23 injuries. However, for the purposes of this module we will only refer to injury risk for our calculations. One hundred female collegiate soccer players are randomly assigned into two groups: an experimental group and a control group.
Lori continued in a part time status working mostly with the Men's Basketball Program until March of 1999, when she joined the Athletic Medicine Staff, working with all the athletes in our program. As a member of the sports medicine pool for the US Field Hockey Association, she often travels with both the men's and women's National Teams. We are always looking for reliable, professional people that are available in the afternoons and evenings. For example, Katz and Fingeroth (1986) investigated the usefulness of the pivot shift, Lachman test, and anterior drawer in diagnosing ACL injury. For a review of reliability coefficients, please go to the Reliability Coefficients Module. For example, the prevalence of depression in the adult population in the United States is about 15 million, or 7 out of every 100 adults (7%). Consider, for example, the prevalence of ACL sprains in the general population compared to the prevalence of ACL sprains in women who see an orthopedic physician after sustaining an acute knee injury. Test findings that result in a large shift from the pre-test to post-test probability are more helpful in the clinical decision making process than tests that result in a small shift.
In this case, the next 40 patients coming in with acute knee pain (note the narrowed population) would be examined using the Declan test and every patient would also have an arthroscopy procedure for a definitive diagnosis. First, the concepts of sensitivity and specificity will be introduced; next wea€™ll apply them in the context of the Declan test.
A 100% sensitive test is depicted in the below figure, where the blue dots represent individuals with the the cervical spine fracture and the green dots represent individuals without the condition: Every single individual with a cervical spine fracture is identified (which for this condition, is desirable). In the case of making a diagnosis of cervical spine fracture using a highly sensitive test, some individuals will be unnecessarily treated as if they have a cervical spine fracture when they do not.
A typical emergency room pattern is to obtain radiographs on every individual presenting with acute cervical pain. More simply, use of the Declan test detects those who have an ACL sprain (as determined via arthroscopy) 74% of the time. Do these values make us more confident that the patient does or does not have an ACL sprain? Conversely, the rate of false positives will also be high, lowering the positive predictive value; it is difficult to find those who actually have the condition.
In the second group (women who went to orthopedic surgeon), the prevalence of ACL injury will be much higher, the negative predictive value will be deflated, and the positive predictive value will be inflated. Because of this, drawing conclusions about the helpfulness of a test by comparing predictive values may lead to incorrect conclusions. Tests that do not add to our diagnostic confidence in at least a small way should not be used in clinical practice.
Today, epidemiology has broadened to include the study of variables such as acute or chronic illness or injury and health status.
All individuals included in the calculations must have been at risk for the entire specified time period. During the fall season, 33 of the soccer players in the conference sustained a grade II anterior talofibular ankle sprain. A unit of exposure is predetermine by the researcher and can include time instances such as one practice, one game, on day of work, one year, etc.
After the season ended, the athletic trainer reported that this soccer team experienced 8 grade II anterior talofibular ankle sprains during the season.
The experimental group undergoes a six-week jump landing training protocol prior to the beginning of a new season. From high school to professional sports, there will always be a need for a dedicated, certified athletic trainer. A positive Lachman and a positive pivot shift were both highly related to the presence of an ACL injury as confirmed via arthroscopy. Contrast this with the prevalence of depression symptoms of 21% in one sample of competitive collegiate student-athletes.
The prevalence in the latter group will be much higher because we have narrowed the population by gender as well as restricted it to those who reported to a specialized physician. If the results of a test do not make us more or less confident that the patient does or does not have the condition or more or less confident that an intervention will be successful, then the test is not helpful. If we knew with more certainty which patients were at high risk of significant injury while not missing anyone with a significant injury, then the number of unnecessary radiographs would be reduced. In other words, it will be easier to find those who do have the condition simply because there are more of them. Perhaps a positive Declan test and a positive Lachman test result in even more compelling likelihood ratios.
The pre-test probability (red dot) tells us that about 1 out of every 5 patients have an ACL sprain. Furthermore, epidemiologists aim to determine the populations that are prone to a particular condition, when and where the condition develops, as well as what factors or exposures are associated with the condition.
Including an exposure measure is useful in comparing incidence across different populations or sports as it allows for a common denominator, by which the values can be compared. Two athletes had a single injury each while the other 5 players each sustained multiple injuries. Once the new season begins, we monitor the number of ACL injuries sustained by the 100 participants.
If we utilized the Canadian C-Spine Rule, which has an extremely high sensitivity of 99.4%, then we would identify almost every single person with a significant cervical-spine injury. The Diagnostic Accuracy of Ruptures of the Anterior Cruciate Ligament Comparing the Lachman Test, the Anterior Drawer Sign, and the Pivot Shift Test in Acute and Chronic Knee Injuries.
But how much does this change our thinking that the patient does or does not have an ACL sprain? Some research reports positive and negative predictive values (PPV and NPV) to help with this interpretation. However, this level of specificity still reduces the number of radiographs by 45% while not missing those who have significant cervical-spine injury.

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