Abstract
Background
Differential diagnosis is a critical systematic approach in auto repair, essential for accurately identifying the root cause from a range of potential issues. This process ensures precise repairs and efficient solutions.
Methods
This guide aims to explore the higher-order thinking involved in mastering differential diagnosis in automotive repair, focusing on advanced techniques and best practices.
Conclusions
For auto repair experts, differential diagnosis is a cornerstone of effective problem-solving. It involves a detailed evaluation of vehicle history, thorough inspection, and analysis of diagnostic data to differentiate between possible causes. This process leads to an accurate understanding of the underlying issue, facilitating targeted and effective repairs. Mastering differential diagnosis enhances communication, improves repair accuracy, and ultimately boosts customer satisfaction. It requires moving beyond basic diagnostic procedures to incorporate higher-order thinking skills, ensuring comprehensive and efficient vehicle servicing.
The Foundation of Effective Auto Repair: Differential Diagnosis
Background
The diagnostic process in auto repair is fundamentally about pinpointing the etiology of a vehicle malfunction. This involves a systematic evaluation of the vehicle’s history, a meticulous physical inspection, and a detailed review of diagnostic data obtained from tools and scans. This process culminates in a precise description of the fault. Analogous to medical practice, differential diagnosis in auto repair serves to enhance communication with vehicle owners, between technicians, and with part suppliers, ensuring everyone is on the same page regarding the vehicle’s issues and required repairs.
Historically, the evolution of diagnostics in auto repair mirrors the advancements in medicine. From basic visual inspections and rudimentary tools, the field has progressed to incorporate sophisticated diagnostic equipment, computer-aided analysis, and comprehensive data interpretation. Similar to the development of the International Classification of Diseases (ICD) in healthcare, the automotive industry relies on standardized diagnostic trouble codes (DTCs) and repair information systems. These systems promote a uniform approach to diagnosing and addressing vehicle problems, facilitating efficient data storage, retrieval, and analysis for evidence-based repairs. They enable consistent communication across workshops, regions, and even countries, ensuring comparable diagnostic data over time. This standardized coding system provides a high level of detail, improving the ability to document vehicle issues and compare repair outcomes across different service centers. At its core, a robust diagnostic system enhances communication among auto repair professionals and is considered a fundamental competency for any skilled technician.
While improved communication and a common language for describing vehicle malfunctions are valuable benefits of diagnostic systems, truly mastering differential diagnosis requires higher-order thinking. This goes beyond simply memorizing DTCs or following basic troubleshooting steps. Higher-order thinking in auto repair involves advanced cognitive skills such as conceptualization, in-depth analysis, and critical evaluation. It encompasses complex reasoning and productive thinking rather than just rote memorization of facts and procedures. Essential skills for higher-order diagnostic thinking include analogical and logical reasoning. Analogical reasoning allows technicians to draw parallels between current issues and past experiences with similar vehicles or problems. Logical reasoning involves using existing knowledge to make informed inferences and solve complex problems. Critical thinking, a crucial component of higher-order thinking, is essential for effectively navigating the complexities of differential diagnosis.
A fundamental understanding of diagnostic principles is a complex, iterative, and essential process for auto repair professionals. This guide emphasizes that mastering differential diagnosis extends beyond merely memorizing diagnostic codes, sensor values, or manufacturer specifications. Specifically, we argue that higher-order differential diagnostic reasoning in auto repair requires a technician to pay close attention to: (1) the limitations of diagnostic tools and data; (2) how focusing solely on fault codes can oversimplify complex issues; and (3) how adopting diverse diagnostic approaches can enhance repair strategies.
Navigating the Pitfalls of Diagnostic Data
Interpreting Diagnostic Metrics and Data
To accurately diagnose vehicle issues, auto repair technicians rely heavily on diagnostic tools and data, from scan tools reading fault codes to multimeters measuring electrical signals. The effectiveness of any diagnostic test or procedure hinges on comparing the results obtained (“index test”) against a known “reference standard” – typically, the expected values or manufacturer specifications for a correctly functioning system. This comparison generates diagnostic metrics, which in the automotive context, could be interpreted as the accuracy and reliability of a specific test or reading in identifying a fault.
While direct equivalents to medical Sensitivity (SN) and Specificity (SP) aren’t commonly used in automotive repair metrics in the same way, the concepts are highly relevant. In auto repair, ‘sensitivity’ could be analogous to the ability of a diagnostic test to correctly identify a fault when it is present. For example, a highly sensitive test for a faulty sensor would reliably flag the sensor when it is indeed malfunctioning. ‘Specificity’, on the other hand, is akin to the ability of a test to correctly indicate the absence of a fault when none exists. A highly specific test would avoid false positives, not indicating a sensor is faulty when it is actually working correctly. Positive Predictive Value (PPV) in automotive terms could relate to the probability that a fault indicated by a test is actually present, while Negative Predictive Value (NPV) would be the probability that the absence of a fault indication correctly means there is no fault. Just as in medicine, focusing solely on these ‘internal metrics’ without considering the broader context can be misleading in automotive diagnostics.
Likelihood Ratios (LR) are more directly applicable to automotive diagnostics. A Positive Likelihood Ratio (LR+) above 1.0 suggests that a positive test result (e.g., a specific fault code) increases the likelihood of the suspected fault being present. Conversely, a low Negative Likelihood Ratio (LR-) close to 0 indicates that a negative test result (absence of a code) strongly suggests the fault is not present. These ratios, influenced by the ‘pre-test probability’ (the technician’s initial suspicion based on symptoms and vehicle history), are crucial for determining the ‘post-test probability’ – the refined likelihood of a specific diagnosis after considering the test results. For example, if a technician suspects a faulty mass airflow (MAF) sensor (pre-test probability) and a diagnostic scan shows a MAF sensor code (positive test), a high LR+ for that code would significantly increase the post-test probability of a faulty MAF sensor.
Current diagnostic approaches often rely on interpreting these metrics – or their automotive equivalents – for individual tests to determine the most probable cause of a vehicle issue. However, this interpretation is fraught with potential pitfalls. Just as in medical diagnostics, relying solely on internal test metrics can be misleading because they don’t always reflect the complex reality of vehicle systems. The automotive equivalents of ‘SPin’ and ‘SNout’ – attempting to ‘rule in’ a diagnosis with highly specific tests or ‘rule out’ with highly sensitive ones – can be outdated and lead to errors. For example, a combination of diagnostic tests might show high ‘specificity’ for a particular fault, meaning when all tests are positive, the fault is highly likely. However, if the ‘sensitivity’ of this test combination is low, it might only identify a small percentage of actual cases of that fault, leading to missed diagnoses in many vehicles exhibiting similar symptoms but not meeting all specific test criteria. This highlights the danger of diagnostic bias, where technicians might prematurely fixate on a diagnosis if a few tests seem to confirm it, overlooking other possibilities. For automotive ‘SPin’ and ‘SNout’ equivalents to be effective, other diagnostic factors must be considered to minimize decision-making errors.
Likelihood ratios, while useful for gauging the change in post-test probability, can also mislead. For example, the effectiveness of a specific sensor test might be well-established in controlled workshop settings. However, in real-world scenarios, factors like vehicle age, environmental conditions, and maintenance history (analogous to ‘prevalence’ in medical contexts) can significantly alter the interpretation of test results. Even if a test shows a high LR+ in ideal conditions, its applicability in a vehicle with high mileage or a history of electrical issues might be different, impacting the certainty of the diagnosis and the subsequent repair decisions. Similarly, relying solely on the absence of ‘red flag’ fault codes (e.g., major engine failure codes) to rule out serious problems can be risky, especially in complex systems where faults might manifest subtly or intermittently, not always triggering obvious codes immediately.
Understanding engine components is crucial for accurate differential diagnosis.
Influence of Testing Conditions and Vehicle Condition
The interpretation of diagnostic data is also heavily influenced by the quality of the diagnostic procedures used. For example, a newly developed diagnostic test for a fuel injector issue, if based on a flawed or poorly designed study, might yield unreliable results that are not reproducible in practice. Technicians must critically evaluate the diagnostic methods they employ, understanding their limitations and potential biases. Factors such as the calibration of tools, the technician’s skill in performing tests, and the specific conditions under which tests are conducted (e.g., engine temperature, load conditions) can all impact diagnostic accuracy. Furthermore, the overall condition of the vehicle significantly affects diagnostic outcomes. Vehicles with advanced wear and tear, extensive modifications, or pre-existing issues might exhibit diagnostic readings that are more complex and harder to interpret. Systems in vehicles with severe malfunctions might show test results that are highly sensitive but less specific, meaning they readily flag issues (high sensitivity) but might also produce more false positives (lower specificity). Conversely, in vehicles with minor or intermittent problems, tests might show lower sensitivity and higher specificity, making it harder to detect subtle faults but more reliable in confirming issues when detected.
Impact on Repair Decision-Making
Effective auto repair decision-making requires a balance between a systematic, data-driven approach (based on diagnostic evidence) and an intuitive approach (relying on the technician’s experience and expertise). Technicians constantly face the challenge of avoiding common pitfalls in diagnostic interpretation. Flaws in understanding the accuracy of diagnostic tools, misinterpreting data probabilities, or relying on low-quality diagnostic procedures can derail the analytical process. Furthermore, intuitive decision-making can be compromised by cognitive biases, such as ‘confirmation bias,’ where a technician favors a diagnosis early on and then selectively interprets subsequent data to confirm that initial hunch, prematurely closing the diagnostic process.
Ultimately, the results of diagnostic tests guide technicians to make decisions about further testing and repair strategies. Therefore, strong clinical reasoning – or in this case, ‘technical reasoning’ – is paramount to effectively link diagnostic findings to an appropriate repair plan within a comprehensive service pathway. Higher-order thinking demands that technicians go beyond simply reading fault codes and consider the broader implications of a diagnostic conclusion. This includes reflecting on the potential costs of misdiagnosis (both financial and in terms of customer satisfaction) and how the chosen repair strategy might affect the vehicle’s long-term reliability and the customer’s overall experience.
Key Takeaway: Most diagnostic data points are ‘internal metrics’ and should not solely dictate repair decisions. Diagnostic data can be skewed by testing methodology and vehicle condition. Even data used for probability assessment, like likelihood ratios, must be applied with a thorough understanding of how factors like vehicle history and condition (‘pre-test probability’ equivalents) can influence outcomes.
The Diagnostic Label Trap: Avoiding Oversimplification
Focusing solely on diagnostic trouble codes (DTCs) and predefined fault descriptions can lead to an oversimplified and potentially misleading approach to auto repair. While DTCs are valuable starting points, relying too heavily on them can result in ‘diagnostic labeling’ that overlooks the complex, interconnected nature of vehicle systems and might not translate into optimal repair outcomes.
Over-reliance on DTCs and Over-diagnosis in Auto Repair
The auto repair industry, like many fields, has increasingly relied on diagnostic tools and metrics to guide decision-making. However, it’s now recognized that this over-reliance on diagnostic labeling can drive unnecessary diagnostic procedures and ‘over-diagnosis’. Over-diagnosis in auto repair occurs when a vehicle is given a diagnostic label for an issue that might never actually cause a problem. This can happen when diagnostic scans detect anomalies or deviations from standard parameters that are statistically flagged as faults but, in reality, are within acceptable tolerances, are normal variations for that specific vehicle, or are unlikely to cause any performance issues or component damage. The core of over-diagnosis lies in how diagnostic labeling is defined and how diagnostic data is interpreted.
When a vehicle presents with performance issues like engine hesitation, poor fuel economy, or unusual noises, technicians often initiate a cascade of diagnostic steps, including reading fault codes, performing sensor tests, and conducting visual inspections to pinpoint the ‘source’ of the symptoms. The auto repair field is susceptible to over-diagnosis due to the abundance of diagnostic data available and the pressure to provide quick, code-based solutions. A significant percentage of diagnostic procedures performed, particularly complex or invasive ones, might be unnecessary if a more holistic and less code-centric approach were adopted. Common examples of potentially over-diagnosed issues include minor sensor readings outside of ‘normal’ ranges, ‘pending’ fault codes that don’t indicate an active problem, or identifying ‘wear and tear’ components as needing immediate replacement when they are still within their functional lifespan.
From a service pathway perspective, over-reliance on diagnostic tests and over-diagnosis can trigger a cascade of potentially inappropriate and costly repairs. This might include replacing parts that are not truly faulty, performing unnecessary system flushes or treatments, or recommending overly aggressive or premature maintenance procedures as first-line solutions. Focusing solely on addressing specific DTCs or perceived faults, without considering the vehicle’s overall condition, driving patterns, and the customer’s needs, might not be the most effective way to choose appropriate first-line repair options. It’s crucial to evaluate whether current diagnostic methods truly lead to better vehicle performance and customer satisfaction.
Evaluating the Link Between Diagnostic Tests and Repair Outcomes
Evidence directly linking specific diagnostic tests to improved vehicle performance and long-term reliability in auto repair is often limited. While diagnostic tools are undoubtedly valuable, their overuse or misapplication can be detrimental. Analogous to studies in healthcare questioning the routine use of imaging, we need to critically assess if routine, code-centric diagnostics always translate to better outcomes in auto repair. Imagine a scenario where replacing a sensor based solely on a DTC doesn’t resolve the underlying performance issue because the problem is actually with wiring or a related component. This illustrates how focusing too narrowly on a diagnostic label (faulty sensor) can lead to ineffective repairs. Similarly, performing a complex engine diagnostic procedure based on a minor, intermittent code might not improve the vehicle’s overall performance or longevity, while significantly increasing repair costs. Another example might be replacing a catalytic converter based on an efficiency code without thoroughly investigating potential upstream issues like engine tuning or exhaust leaks, which might be the root cause of the code.
These examples highlight that simply adding more diagnostic tests or relying heavily on DTCs in the repair process doesn’t automatically lead to better vehicle outcomes. It can contribute to over-diagnosis and the unnecessary use of subsequent, potentially costly, repairs. Future advancements in auto repair should focus on developing diagnostic methods, classification systems, and repair algorithms that improve the entire service pathway, leading to enhanced vehicle performance and customer satisfaction, without exposing customers to the drawbacks of over-diagnosis and unnecessary repairs. In other words, knowing the exact DTC or sensor reading might not always change the downstream decision-making regarding the most effective, high-quality first-line repair options needed to truly improve vehicle performance and reliability.
Diagnostic tools are essential, but must be used judiciously to avoid overdiagnosis.
Prognostic Thinking: Considering Vehicle Longevity and Preventative Care
‘Prognosis’ in auto repair, while not commonly used in the same terminology as in medicine, is equally important. It involves assessing the likelihood of future vehicle issues and planning for preventative maintenance. Prognostic thinking in auto repair asks whether a diagnostic finding or a repair decision will positively influence the vehicle’s future reliability and longevity. Just as in healthcare, ‘no intervention’ or a ‘wait-and-see’ approach can be as valid a decision as immediate repair in certain situations. Failing to incorporate prognostic thinking into auto repair can lead to unnecessary repairs, increased costs, and potentially decreased customer satisfaction.
Much of auto repair training focuses on the principles of diagnosing current faults and applying immediate fixes. Historically, the emphasis has been on informing technicians and vehicle owners about the causes of malfunctions and how to reach a diagnosis and prescribe effective repairs linked to that diagnosis. We argue that placing equal emphasis on ‘prognostic diagnostics’ – considering the future health of the vehicle – could reduce over-diagnosis and over-treatment. For instance, recommending ‘watchful waiting’ and regular monitoring for minor issues that often resolve themselves or don’t immediately impact vehicle safety or performance can reduce the risk of unnecessary repairs, associated costs, and potential customer anxiety. By accurately predicting potential failure trajectories, technicians can develop personalized maintenance plans more likely to improve long-term vehicle reliability and customer satisfaction. This could involve determining which vehicles truly require immediate, intensive repairs versus those that can benefit from proactive maintenance and monitoring, thus optimizing repair costs and service schedules.
Interestingly, vehicle owners often prefer advanced diagnostic procedures and extensive repairs, perceiving them as more thorough and reassuring, even if they don’t necessarily lead to improved long-term vehicle outcomes. This presents a challenge for auto repair professionals. Just as in healthcare, receiving a detailed diagnostic label and undergoing extensive repairs can have financial consequences and increase the burden on the customer, even if the repairs are not strictly necessary or beneficial. Customers are often unaware of the potential downsides of over-diagnosis and unnecessary repairs. Given that many minor vehicle issues might be self-limiting or not significantly impact vehicle lifespan, we need to explore how a ‘watchful waiting’ approach, combined with proactive maintenance advice, can be effectively integrated into auto repair practices.
Key Takeaway: An overly zealous pursuit of a specific diagnostic label can lead to over-diagnosis and subsequent unnecessary repairs. Focusing on prognosis – the vehicle’s future health – and considering less invasive or immediate interventions for minor issues can improve overall repair effectiveness and customer satisfaction.
Beyond Diagnostic Labels: Adopting Phenotyping for Enhanced Vehicle Management
We’ve shown how current diagnostic labels in auto repair can sometimes negatively impact repair outcomes due to oversimplification and over-diagnosis. To bridge the gap between diagnosis and truly effective repairs, we must embrace the complexity and variability inherent in vehicle systems, moving beyond generic diagnostic labels. ‘Phenotyping’ – classifying vehicles based on a broader set of characteristics – offers a superior method for understanding and managing vehicle issues.
Traditionally, ‘phenotype’ refers to the observable characteristics of an organism resulting from the interaction of its genes and environment. In a modified sense for auto repair, phenotyping can encompass a vehicle’s physical characteristics, operational history, maintenance records, sensor data patterns, and environmental factors to create a more nuanced profile beyond a simple diagnostic code. Phenotyping, examining the interplay between vehicle design, usage patterns, and environmental conditions, can be used to predict potential issues or tailor maintenance schedules. For example, vehicles frequently driven under heavy loads or in harsh climates might be phenotyped as ‘high-stress’ vehicles, requiring more frequent inspections of specific components.
In the context of vehicle systems, phenotyping can be based on a combination of factors. Drawing parallels from medical research on osteoarthritis phenotyping, we can categorize vehicles based on: vehicle age and mileage (analogous to radiological grades of OA), engine power and drivetrain configuration (muscle strength), vehicle weight and load capacity (body mass index), history of repairs and malfunctions (co-morbidities), driver behavior and usage patterns (psychological distress), and patterns of sensor data deviations (alteration of pain neurophysiology). Phenotypes could be named based on these characteristics, such as ‘low-mileage, well-maintained’, ‘high-mileage, heavy-use’, ‘performance-tuned’, or ‘economical commuter’ within the same general ‘diagnostic label’ like ‘engine performance issue’.
Other phenotyping approaches could focus on specific vehicle subsystems. For instance, ‘pain susceptibility phenotypes’ in knee OA research could be analogous to ‘system vulnerability phenotypes’ in vehicles. These could be based on measures like component stress levels, operating temperatures, or electrical signal stability (analogous to pressure pain threshold and temporal summation). A ‘high-stress electrical system’ phenotype might indicate a vehicle particularly prone to electrical faults over time.
Another approach could involve identifying ‘trajectories’ of vehicle performance over time. Just as pain and function trajectories are studied after knee replacement, we could analyze vehicle performance data (fuel economy, emissions, fault code frequency) over years of service. Subgroups of vehicles might show patterns of persistent performance decline or specific component failures after a certain mileage. These trajectories could be predicted by factors like vehicle usage history, maintenance schedule adherence, and even geographic location.
In the context of engine diagnostics, one could identify ‘performance trajectory’ phenotypes based on patterns of fuel consumption, emissions readings, and power output over time. Vehicles exhibiting rapid performance decline or consistently poor emissions could be phenotyped differently from those with stable, gradual degradation. Similarly, ‘subgroups’ within a general ‘engine misfire’ diagnosis could be identified based on a detailed history and physical examination of the engine – including compression tests, injector balance tests, and sensor readings. While these subgroupings might be more complex to establish initially, they could offer more precise diagnostic insights and potentially lead to more targeted and effective repair strategies. Further research should aim to determine if these phenotyped subgroups respond better to specific, tailored repair approaches.
In a fleet of vehicles with ‘non-traumatic suspension complaints’, one could identify ‘suspension wear trajectories’ over two years based on metrics like shock absorber performance, tire wear patterns, and alignment data. Prognostic variables, such as driving conditions and maintenance frequency, could predict vehicles likely to follow a ‘continuous high wear’ trajectory. For vehicles with ‘braking system issues’, phenotypes like ‘high-performance braking’, ‘economy braking’, and ‘heavy-duty braking’ could be classified based on clinical measures such as brake pad wear rate, rotor condition, and brake fluid analysis. These phenotypes could then be used to develop targeted maintenance and repair approaches, improving vehicle safety and longevity.
These examples illustrate that several distinct phenotypes can exist within a ‘single’ diagnostic label in auto repair. This implies that vehicles under the same diagnostic category might respond differently to the same repair. Furthermore, the effectiveness of diagnostic tests and repair procedures might vary significantly depending on the vehicle’s phenotype.
Key Takeaway: Phenotyping, based on vehicle characteristics, performance history, and operational data, can provide a more nuanced understanding of vehicle issues beyond simple diagnostic labels. This approach allows for the identification of distinct vehicle profiles and performance trajectories within a given diagnostic category.
Conclusion
Higher-order thinking – diagnostic reasoning that transcends rote memorization of codes and procedures – is essential for auto repair experts. This guide has explored how higher-order thinking can mitigate errors in interpreting diagnostic data, reduce over-diagnosis, and acknowledge that a single diagnostic label can encompass diverse vehicle phenotypes. Moving forward, auto repair diagnostics must evolve beyond basic fault codes and embrace phenotyping and prognostic evidence to enable more targeted and effective vehicle care, ultimately improving customer outcomes. We are just beginning to understand the diverse profiles of vehicles presenting with similar issues. Large vehicle databases, telematics data, and advanced data analysis tools like artificial intelligence will accelerate our understanding of the complex relationship between diagnostics and vehicle outcomes, paving the way for a more personalized and predictive approach to auto repair.
Conflicts of interest
The authors declare no conflicts of interest.
References
[1] The diagnostic process involves identifying or determining the etiology of a disease or condition through evaluation of patient history, physical examination, and review of laboratory data or diagnostic imaging; and the subsequent descriptive title of that finding.
[2] Walker.
[3] International List of Causes of Death (ICD).
[4] Higher order cognitive skills include conceptualization, analysis, and evaluation, and involves ordered levels of reasoning containing productive thinking, or reasoning, versus learned, or reproductive, thinking.
[5] Fundamental skills involved with higher order thinking includes analogical and logical reasoning.
[6] Diagnostic tests such as clinical examination or imaging. The main characteristic of a diagnostic study is the comparison of a test (or combination of tests) called the “index test” to a known “reference standard”. This produces the test metrics.
[7] Test metrics.
[8] A LR+ above 1.0 influences post-test probability with a positive finding, whereas a low LR− (a value close to 0) influences post-test probability with a negative finding.
[9] SPin and SNout are purported to rule in with high specificity and rule out with high sensitivity; however, this concept is outdated and can lead to interpretation errors.
[10] A study on the diagnosis of subacromial pain demonstrated that combining three clinical features reached 100% SP, but only 9% SN.
[11] The accuracy of the Lachman test for anterior cruciate ligament (ACL) tear was established in primary care and orthopedic cohorts.
[12] Orthopedic cohorts.
[13] High LR+.
[14] Authors have recently questioned the utility of history elements to rule out serious cause of low back pain (poor negative LR).
[15] Poor negative LR.
[16] The Thessaly test for meniscal tears was initially developed in a study with a low-quality design and the results were not replicated afterward.
[17] Not replicated afterward.
[18] Clinicians should look closely at study designs, reference standards and how the tests were described as these have most commonly influenced biases in diagnostic accuracy studies.
[19] Decision-making models propose a balance between the analytical approach based on evidence (e.g., test metrics), and the intuitive approach that relies on the experience of the evaluator.
[20] Flaws in interpreting the accuracy of a test, poor understanding of probabilities and low-quality evidence can derail the analytical process. The intuitive process can be overridden by verification or confirmation biases such as anchoring or premature closure in which a favored diagnosis is found and the clinician stop the diagnostic process too early when the scheme seems fitting.
[21] Most fields in medicine have relied on diagnostic tests and metrics to inform clinical decision-making.
[22] Overdiagnosis occurs when a patient receives a diagnostic label that may have never caused them harm.
[23] Diagnostic tests identify abnormalities or risk factors that most often will not cause symptoms or impairments.
[24] When a patient presents with symptoms of pain to the spine, knee, hip or shoulder, clinicians often initiate a cascade of history questions, physical examination tests, clinical measures and imaging tests in order to diagnose the sources of the symptoms.
[25] Up to 50% of all imaging test referrals are considered inappropriate.
[26] “lumbar degeneration”, “disk bulges”.
[27] “disk herniation”.
[28] “degenerative meniscal tears”.
[29] “degenerative labral tears”.
[30] “subacromial bursal thickening” or “rotator cuff tendinosis”.
[31] Receiving a diagnostic label may have physical, psychosocial and financial consequences as well as increasing treatment burden, exposure to unnecessary tests and treatments and adverse events that lead to dissatisfaction with care.
[32] A meta-analysis explored the effect of routine diagnostic imaging on patient reported outcomes for patients with musculoskeletal disorders. The authors found 11 trials for low back pain and knee complaints that provided moderate evidence that using routine diagnostic imaging was not beneficial to improve pain.
[33] Adding MRI in primary care for younger patients with traumatic knee complaints did not improve knee-related function after one year.
[34] McKenzie.
[35] Movement system.
[36] Prognosis is a method of classification which is designed to determine the likelihood something happening in the future.
[37] Phenotype has been used to reflect the observable properties of a particular organism that are produced from an interaction of the genotype and the environment. Science has modified phenotyping to include physical, biochemical, and genetic characteristics along with interactions with the environment that have produced observable, unique characteristics.
[38] Single nucleotide polymorphisms.
[39] Osteoarthritis Initiative study.
[40] Amsterdam OA cohort.
[41] Multicenter Osteoarthritis Study.
[42] Trajectories of knee pain and function following total knee arthroplasty over up to 5 years in a cohort of 689 patients.
[43] Pain trajectories over 12 weeks in 1585 patients consulting for low back pain (recovery at week 2 or 12, pain reduction without recovery, fluctuating pain and high-level pain for 12 weeks).
[44] Up to nine subgroups using 112 characteristics based on history and physical examination of patients consulting for low back pain in primary care.
[45] Disability trajectories at 2-year using Disabilities of the Arm, Shoulder and Hand questionnaire (DASH).
[46] Three subgroups classified as “strong”, “weak and tighter” and “weak and pronated foot” based on six common clinical measures such as flexibility, strength, patellar mobility and posture of the foot.
