Every student has a varied level of mathematical proficiency. Therefore, it is important to provide them with questions accordingly. Owing to advances in technology and artificial intelligence, the Learning Management System (LMS) has become a popular application to conduct online learning for students. The LMS can store multiple pieces of information on students through an online database, enabling it to recommend appropriate questions for each student based on an analysis of their previous responses to questions. Particularly, the LMS manages learners and provides an online platform that can evaluate their skills. Questions need to be classified according to their difficulty level so that the LMS can recommend them to learners appropriately and thereby increase their learning efficiency. In this study, we classified large-scale mathematical test items provided by ABLE Tech, which supports LMS-based online mathematical education platforms, using various machine learning techniques according to the difficulty levels of the questions. First, through t-test analysis, we identified the significant correlation variables according to the difficulty level. The t-test results showed that answer rate, type of question, and solution time were positively correlated with the difficulty of the question. Second, items were classified according to their difficulty level using various machine learning models, such as logistic regression (LR), random forest (RF), and extreme gradient boosting (xgboost). Accuracy, precision, recall, F1 score, the area under the curve of the receiver operating curve (AUC-ROC), Cohen’s Kappa and Matthew’s correlation coefficient (MCC) scores were used as the evaluation metrics. The correct answer rate, question type, and time for solving a question correlated significantly with the difficulty level. The machine learning-based xgboost model outperformed the statistical machine learning models, with a 85.7% accuracy, and 85.8% F1 score. These results can be used as an auxiliary tool in recommending suitable mathematical questions to various learners based on their difficulty level. Mathematics is the most fundamental subject taught in elementary schools because it helps in developing critical thinking and logical reasoning. It is important to know that every student has a different set of skills and proficiency for learning mathematical concepts and that they are provided with questions in accordance with their individual abilities. Owing to advancements in technology and artificial intelligence, the most popular way of interacting and communicating with students through online learning is using the Learning Management System (LMS). This system uses an online database to store information on students. Thus, it can recommend appropriate questions to each student based on previous questions they have solved. The LMS is an online learning platform that can systematically evaluate and monitor learners’ skills .It is important to ask learners appropriate questions to evaluate their abilities. In numerous previous studies, questions were classified after considering various variables, ensuring that the most suitable question would be suggested next for the learner. Jayalakshmi and Sheshasaayee  and Ray However, most previous studies on question classification have focused on small-scale datasets using the conventional method of analysis to accompany their experimental and theoretical assumptions. Although their theories are significant and applicable for small sets of data, they become ineffective when applied to large-scale data because they require the incorporation of a more advanced algorithm to classify more complex questions. Unlike the limitations of using statistical analysis on large-scale data, machine-learning models are more powerful and capable of solving high-dimensional problems that are difficult for humans to resolve. Therefore, there is an urgent need to classify large-scale questions using an improved method for classifying problems based on machine learning.In this study, we used various machine learning models, such as logistic regression (LR) , random The main contributions of this study are given as follows:The most relevant features found through the statistical t-test analysis were answer rate, question type, and solving time; these variables correlated most significantly with the difficulty level.To compare the performance of the several machine learning models more reliably, we considered seven evaluation metrics.The xgboost model outperformed the other machine-learning models in the multi-class classification problem with the maximum accuracy, F1-score, Kappa and MCC scores.The remainder of this manuscript is sectioned as follows: In Section 2, we have explained the concept of question difficulty and how it can be applied using a machine learning approach for readjusting the new difficulty levels of questions. Section 3 introduces the overall data pipeline process and the construction of the machine-learning model and describes the evaluation metrics we used for assessing the results of our experiment. Section 4 describes the results of feature selection based on statistical analysis. We have also described the results of the model performance based on several machine-learning models. In Section 5, the conclusions are presented.Many previous studies have explored the concept of item difficulty levels. According to one study, item difficulty level is defined as the percentage of students who choose the correct answer out of all other students, as well as a scale that measures the degree of difficulty experienced by students when solving a set of problems. The level of question difficulty can be affected by the characteristics of the question or the type of learning or problem-solving model used , 12.Other researchers have differentiated difficulty levels by examining socio-psychological perspectives. For example, Kim  has explSimilarly, Watering and Rijt  inferredThere is currently a trend toward using artificial intelligence to classify difficulty levels instead of the socio-psychological methods conventionally used. In other words, learning patterns can be analyzed by identifying learners’ behaviours through a quantitative analysis of questions. Zhao et al.  proposedThe scope of machine learning can be broken down into three main sub-fields, namely supervised learning, unsupervised learning, and reinforcement learning, each of which plays an important role in the field; therefore, when learning the basics of artificial intelligence, each should be thoroughly explored in equal measure. For our analysis, we focused on using the supervised learning approach. In supervised learning, a trained model is used to learn multiple features that can be classified into multiple classes or labels from the observed data; then, a new label value is predicted based on the prediction value that most accurately matches the actual target variable.In most recent research, machine learning approaches have been used to solve multi-classification problems; these approaches have shown excellent performance in various domains. In a study by Petersen and Ostendorf , the difAn LMS is an online solution platform that manages learners and evaluates their skills through various online lecture courses. In our study, we analyzed the dataset provided by our collaborator ABLE Tech, a mathematical LMS-based solution platform targeting teenagers from elementary to high school.The workflow system architecture used in this study, which consists of two parts, is shown in When constructing the master table, certain students were excluded because information on them was unavailable. Initially, the provided data comprised 8.15 million rows; after preprocessing, only 3.12 million rows were used for the actual analysis. Additionally, it comprised information on students of various grades who responded to each mathematical question based on a unique ID value . Since there were various grades of students for each question, the standard solution time could also be extracted as a new variable. In this study, the average and median solution times were compared and applied to a new variable as the median value, meaning that the question that was solved most frequently could be obtained using the problem ID. In the table wherein the questions have been sorted according to this frequency, the rate of correct answers that students solved for each problem ID could also be calculated as a new variable. The rate of correct answers refers to the percentage of students who correctly answered each question; each question had been divided into three components, namely chapter, episode , chapter , and chapter type . The response options for the mathematical questions were largely divided into subjective and multiple-choice types. For the target variable, the difficulty of the existing labelled question was re-labelled to “high”, “medium”, or “low” as a multi-category for analysis, where “high” and “low” mean that the item is of high and relatively low difficulty, respectively. The seven final variables used in this study were the rate of correct answers, major chapter, intermediate chapter, small chapter, solution time, type of correct answer, and difficulty. The final data sample is shown in In this study, we examined and analyzed the multi-class classification problem in relation to the level of difficulty in solving mathematical problems. Instead of using all the various classifier models, we focused on using optimal machine learning algorithms that had been employed in related research and were well-suited to our field of research. The following models were implemented and tested: logistic regression (LR), k-nearest neighbors algorithm (kNN), stochastic gradient descent (SGD), classification and regression tree (CART), support vector machine (SVM), multi-layer perceptron (MLP), random forest (RF), extra trees ensemble classifier (ET), adaboost (ADA), categorical boosting (CatBoost), gradient boosting machine (GBM), light gradient boosting machine (LightGBM), and extreme gradient boosting machine (XGB).p(y = 1|x) represents the probability of the binary outcome (y = 1) given the independent variables (x), and z = b0 + b1x1 + b2x2 + … + bnxn represents the linear combination of the independent variables (x) and their coefficients (b), where b0 is the intercept and b1 to bn are the coefficients of the independent variables x1 to xn. The function e is the base of the natural logarithm. The logistic function on the right-hand side of the equation (1/(1 + ez−)) maps the linear combination of the independent variables (z) to a probability value between 0 and 1, which represents the predicted probability of the binary outcome (y = 1) given the independent variables (x). The logistic function is also called the sigmoid function, and it has an S-shaped curve that is useful for modelling probabilities.Logistic regression (LR) is a statistical method that models the relationship between a dependent variable (binary outcome) and one or more independent variables (predictors). The dependent variable is represented as a binary variable (0 or 1) and is modelled using a logistic function. The logistic function transforms the linear combination of the independent variables into a probability value between 0 and 1. The following model can be represented mathematically as follows:yi is the output of the i-th neighbor, and The k-Nearest Neighbor (kNN) algorithm is a simple and popular machine learning model used for classification and regression tasks. The algorithm works by finding the k closest data points in the training set to the input data point and predicting the output based on the majority vote of the k neighbors for classification, or the average value of the k neighbors for regression. The kNN model defines the distance between two data points as a metric function, such as the Euclidean distance or Manhattan distance. The prediction of the model can be written as:θ are the model parameters, η is the learning rate, J is the cost function for a single training example (xi), and ∇J is the gradient of the cost function with respect to the model parameters for a single training example. The update is performed for each training example in the dataset.Stochastic Gradient Descent (SGD) is an iterative optimization algorithm used to minimize the cost function in machine learning models. It works by updating the model parameters for each training example, unlike batch gradient descent which updates parameters based on the entire training dataset. SGD is more efficient and less computationally expensive than batch gradient descent and is widely used in deep learning models. The mathematical equation for updating the parameters in SGD can be represented as:yi is the true target value for the i-th sample, pi is the proportion of samples that belong to the i-th class in the subset of data at that node.Classification and Regression Trees (CART) is a decision tree algorithm used for both regression and classification problems. It creates a binary tree structure where each internal node corresponds to a decision based on a feature and each leaf node corresponds to a prediction. The algorithm recursively partitions the data into smaller subsets based on the best split at each node. The splitting criterion used in CART is the reduction in variance or the Gini index for regression and classification problems, respectively. The optimal split is chosen based on the criterion that maximizes the reduction in variance or the Gini index. The algorithm stops when a stopping criterion is met, such as reaching a maximum tree depth or having too few samples at a node. Mathematically, the CART algorithm seeks to minimize the mean squared error (MSE) for regression problems:xi, yi), where xi is a feature vector and yi is the corresponding target class label, it seeks to find the hyperplane wT x + b = 0 that separates the data into two classes:w|| is the L2-norm of the weight vector, and the constraint ensures that the data is correctly classified and lies outside the margin. It is known for its ability to handle high-dimensional data and non-linear decision boundaries through the use of kernel functions. However, it can be sensitive to the choice of hyper-parameters and is not suitable for large datasets.Support Vector Machines (SVM) is a machine learning algorithm used for classification and regression problems. The algorithm tries to find the best hyperplane that separates the data into different classes while maximizing the margin between the hyperplane and the nearest data points of each class. Mathematically, given a set of training data  or cross-entropy. MLPs are commonly used for classification and regression problems, and their performance can be improved by increasing the number of layers, adding regularization techniques, or using different activation functions. However, they can be sensitive to the choice of hyper-parameters and prone to over-fitting if the number of parameters is too large.A Multi-Layer Perceptron (MLP) is a feed-forward artificial neural network that consists of multiple layers of nodes, where each node in one layer is connected to every node in the subsequent layer. The nodes in the MLP are called neurons, and they apply a non-linear transformation to the input data. Mathematically, given a set of input data x, the output of the MLP is calculated as follows:ft(x) is the prediction of the t-th decision tree for the input x. The randomness introduced in Random Forest helps to reduce over-fitting and improve the accuracy of the model.Random forest (RF) is an ensemble learning method that combines multiple decision trees to make predictions. It is used for both regression and classification problems, and is known for its high accuracy and robustness to noise and over-fitting. The output of the algorithm is the average or majority vote of the individual trees. Mathematically, the output of a Random Forest model for a new sample x is:F(x) is the model’s prediction for a new input, n is the number of trees in the ensemble, and fi(x) is the prediction of the ith decision tree in the ensemble. Each decision tree is built using a random subset of the features and a random threshold value for each split. The final prediction is determined by averaging the predictions of all the trees in the ensemble.Extra Trees (ET) ensemble classifier is a machine learning algorithm that combines multiple decision trees to create a predictive model. ET builds on the Random Forest algorithm, but with some key differences. Like Random Forest, ET creates multiple decision trees using bootstrap samples of the training data and random subsets of the features. However, it also selects the splitting point at each node of the tree using a random threshold value, rather than finding the best threshold. This randomization can result in less bias and variance in the model, and can improve the accuracy. The mathematical equation for ET is similar to that of a decision tree ensemble:F(x) is the final strong classifier, ft(x) is the weak classifier at iteration t, and αt is the weight assigned to the weak classifier. The weight αt is calculated based on the performance of the weak classifier on the training data, and is higher for more accurate classifiers. During training, AdaBoost assigns initial weights to each sample in the training set. These weights are then updated at each iteration based on the accuracy of the weak classifier on the training data. The final output of the strong classifier is determined by combining the weighted outputs of the weak classifiers. AdaBoost is a powerful algorithm that can achieve high accuracy with relatively few weak classifiers. However, it can be sensitive to outliers and noise in the data.AdaBoost (Adaptive Boosting) is a popular boosting algorithm that combines multiple weak classifiers to form a strong classifier. The basic idea behind AdaBoost is to iteratively train weak classifiers on the training data, and assign higher weights to the misclassified samples at each iteration. This allows the subsequent weak classifiers to focus more on the previously misclassified samples. Mathematically, the AdaBoost algorithm can be expressed as follows:Fm(x) is the model’s prediction at iteration m, Fm−1(x) is the prediction at iteration m − 1, γm is the learning rate for iteration m, and hm(x) is the weak model at iteration m. However, CatBoost uses a unique algorithm for handling categorical data, which involves splitting the categorical features into ordered groups and encoding them as numerical values. This approach can result in more accurate and efficient models for datasets with categorical features.Categorical boosting (CatBoost) is a gradient boosting algorithm that is designed to handle categorical features in machine learning problems. It uses a combination of ordered boosting, categorical features processing, and gradient-based decision tree algorithms to achieve high accuracy with categorical data. CatBoost also includes a set of techniques to prevent over-fitting and handle missing data. The mathematical equation for CatBoost is similar to other gradient boosting algorithms:Fm(x) is the model’s prediction at iteration m, Fm−1(x) is the prediction at iteration m − 1, γm is the learning rate for iteration m, and hm(x) is the weak model at iteration m. The weak model is usually a decision tree that predicts the residuals of the previous model. The GBM algorithm iteratively adds new weak models to the ensemble until the desired level of accuracy is achieved.Gradient Boosting Machine (GBM) is a machine learning algorithm that creates a predictive model by combining multiple weak models. GBM works by iteratively fitting new models to the residuals of the previous models, and combining their predictions to improve the overall accuracy. GBM is a powerful algorithm that can handle a variety of data types and is commonly used for regression and classification problems. The mathematical equation for GBM can be represented as:Similar to the gradient boosting machine (GBM) framework, LightGBM is the lightweight version of the gradient boosting mechanism and it is designed to be highly efficient and scalable, making it well-suited for large-scale machine learning applications. LightGBM uses histogram-based algorithms for computing gradients, which reduces memory usage and computation time. It also uses a leaf-wise tree growth strategy, which can lead to better accuracy with fewer trees. The mathematical equation for LightGBM is similar to the standard gradient boosting algorithm. However, LightGBM also introduces a new feature called “leaf-wise growth,” which means that the tree is grown by splitting the leaf that results in the largest reduction in the loss function, rather than the traditional level-wise approach. This results in a more accurate and efficient model.θ represents the model parameters, l is the loss function, yi is the actual value, fk) is a regularization term that penalizes complex models. The main goal is to accurately predict the target variable based on a large number of features. Given the brief summary of each machine learning-based models, hyper-parameter tuning is important for achieving optimal performance under each machine learning method. Accordingly, we used GridSearchCV to automatically find the optimal parameter values using the Python-based scikit-learn package. GridSearchCV is a lattice search technique that solves the complexity of machine learning models and reduces the repetitive computation time required to find optimal parameter values [Extreme Gradient Boosting (XGBoost) is a machine learning model that uses a gradient boosting framework to improve the performance of decision trees. The xgboost model is designed to handle large-scale datasets and is known for its speed, accuracy, and robustness. The xgboost model combines the outputs of multiple decision trees to make more accurate predictions. Each decision tree is trained on a subset of the data and the residuals of the previous trees, and the final prediction is made by aggregating the predictions of all the trees. The xgboost model uses a loss function to measure the difference between the predicted and actual values, and the gradient of the loss function is used to update the parameters of the model. The objective function of xgboost is defined as:r values –21. TablTo compare the model’s performances, different evaluation criteria were used. Accuracy was used as an index to evaluate the similarity of the predicted data with the actual data and thereby determine the accuracy of the model. The confusion matrix, which is used as a performance indicator in binary classification, is an indicator that shows the level of confusion in a trained classification model when calculating the predicted values. The one-vs-rest (OVR) method was used to calculate the confusion matrix in this study, and the results were used for multiple classification. In other words, after being divided into “high”, “medium”, or “low” based on the difficulty levels, the questions could be classified into two groups, such as “high” and “middle/low,” “middle” and “high/low,” and “low” and “high/middle,” and the final value would be calculated as the average value for each category. In the case of an unbalanced dataset, the reliability could be further improved by measuring the values of precision and recall, which have been recommended more than the accuracy score has in existing research. Precision and recall can be evaluated intensively for the predictive performance of positive datasets by using the true-positive (TP), false-positive (FP), and false-negative (FN) values calculated from the confusion matrix. The precision and recall values were calculated using the following equations:po is the observed proportion of agreements between the raters and pe is the proportion of agreements expected by chance. po is calculated as the number of observed agreements between the raters divided by the total number of ratings, while pe is calculated as the product of the marginal proportions of each rater’s categories. It ranges from -1 to 1, where values closer to 1 indicate higher agreement between the model and the ground truth labels. Also, MCC is another metric that takes into account the true positives, true negatives, false positives, and false negatives. MCC can be calculated by using the concepts of confusion matrix metrics. The following equation computes the MCC score:The F1 score is an index combining both precision and recall. This index is used to evaluate the balance between precision and recall; the closer it is to 1, the better the performance. Finally, the AUC-ROC curve was used to evaluate the model’s performance. Using this curve, the change in TP ratio  and the change in FP ratio  can be visually evaluated. In addition, due to the imbalanced dataset in terms of difficulty levels, comparing our models with accuracy and F1 scores may not be sufficient enough to justify which model is the best model. In this case, we additionally used two statistical methods of Cohen’s Kappa and Matthew’s Correlation Coefficient (MCC) metrics to further investigate and analyze our model comparisons. Cohen’s Kappa measures the agreement between two raters  by accounting for the agreement that could occur by chance. The formula for Cohen’s Kappa is as follows:The difficulty levels of mathematical questions can be evaluated on a numerical or character basis. In this study, the difficulties of the questions were classified based on the characteristics of the questions. Given the provided data, the values in the “multi-difficulty” variable for each question ranged from 0 to 10; these were converted and labelled as “high,” “medium,” or “low” based on the given range of scores.The data on the mathematical questions used for the multi-class classification problem were obtained from 2015 to 2022, and they comprised approximately 320,000 rows. In this study, there were about 17,050 questions grouped by question IDs , and the grade range of students was from the 3rd grade of elementary school to the 2nd grade of high school; elementary, middle, and high school students accounted for 51.4, 47.8, and 0.83% of the total number of students, respectively, with there being more elementary and middle school students than high school students. Accordingly, there were more questions corresponding to elementary and middle school students; brief information on the variables used in the experiment is shown in A statistical t-test analysis was performed to verify the results of feature selection between the candidate variables. Variables other than answer type influenced question difficulty among the independent variables. Variables other than answer type were significant at a significance level of ±5%, and all the p-values were smaller than 0.05. The results of the significance tests are presented in In this study, considering the characteristics of various items, we attempted to classify item difficulties. A statistical t-test analysis was conducted to verify whether the characteristics of each item had a significant effect on its difficulty. The analysis revealed a positive correlation between the rate of correct answers for the items, each section of the item , and the solution time. However, there was no correlation with the answer type of the item; therefore, this characteristic was excluded from our analysis. The rate of correct answers for each item, each section of the item, and the solution time had p-values less than 0.05, supporting the assumptions in our experiment. Additionally, the difficulty of the mathematical question items was classified using various machine learning techniques. The xgboost model showed superior performance to those of statistical machine learning techniques, such as logistic regression, support vector machine, random forest, decision tree-based models, and k-nearest neighbor models. The xgboost model also had the best accuracy, F1, AUC-ROC, Kappa and MCC of 85.7%, 0.858, 0.961, 0.784 and 0.785 respectively.


Bilateral femoral neck stress fractures are relatively rare injuries that occur frequently in military recruits, athletes and patients with osteoporosis, renal bone disease, metabolic bone disease, and chronic steroid use. Herein, a case of an elderly patient with bilateral femoral neck stress fractures is reported.A 65-year-old man presented to the author’s hospital with right hip pain for over a month. The patient was a farmer, had a long history of field labor before the onset of pain, denied any history of trauma.The patient was diagnosed with a right subcapital fracture of the femoral neck after examination. The patient complained of only right hip symptoms, and hip computed tomography showed no abnormalities in the left hip. A tension fracture of the left femoral neck was missed due to unawareness of the abnormal signal of the left femoral neck seen on right hip magnetic resonance imaging.During the first hospitalization, the patient underwent total hip arthroplasty (THA) on the right hip. Two months after the operation, the patient started to have pain in the left hip and underwent left THA again for a displaced left femoral neck fracture.The patient eventually underwent bilateral THA surgery and had a satisfactory functional recovery. But the oversight in the diagnostic process led to the patient undergoing left THA that could have been avoided.For patients who complain of hip pain but deny a history of trauma, we should be concerned about the presence of a hip fracture even if the patient’s radiograph does not report a positive result. The most sensitive method is bilateral magnetic resonance imaging examination of the hip. Femoral neck stress fractures require early diagnosis and treatment to prevent complications. Fullerton and Snowdy classified stress fractures of the femoral neck as nondisplaced fractures on the compression side, nondisplaced fractures on the tension side or displaced fractures. Additionally, stress fractures can also be subdivided into fatigue fractures, where a normal bone is unable to respond adequately to abnormal stress (overload or overuse), and insufficiency fractures, where abnormal bone is unable to withstand normal stress. The most common symptom of a femoral neck stress fracture is pain in the hip and the inability to stand. Stress fractures in elderly patients often manifest as displaced fractures, which may be related to the decline in pain sensitivity in elderly individuals and inactive therapeutic interventions due to economic reasons. The treatment method for a femoral neck stress fracture needs to be considered according to the patient’s physical condition, economic condition, age, fracture type and other factors. The general treatment methods include conservative treatment, internal fixation with cannulated screws, and sliding hip screw fixation, but patients with hip joint deformities are generally recommended to undergo valgus osteotomy, hip replacement  and so on.According to the literature, stress fractures mostly occur in athletes, soldiers or elderly individuals. Femoral neck fractures represent 11% of all stress fractures in athletes.We report the case of an elderly patient with bilateral femoral neck stress fractures. The first right THA was performed for the right subcapital fracture of the femoral neck. At that time, the nondisplaced left femoral neck fracture on the tension side was missed. Two months after the operation, the patient underwent left THA again for a displaced left femoral neck fracture.A 65-year-old Chinese male presented at our hospital with a 1-month history of onset of right hip pain. The pain was tolerable at that time, and the pain worsened 2 weeks later. A pelvic X-ray showed a fracture of the right femoral neck. The patient was a farmer, and he denied a recent history of trauma, glucocorticoid intake or radiotherapy. He had been a smoker and heavy drinker for more than 20 years.The blood examination and biochemical laboratory values were within normal limits. Whole-body bone mineral densitometry (BMD) was measured by dual-energy X-ray absorptiometry to assess the mineralization status of bones. BMD measured at the L1–L4 level showed severe osteoporosis with a T score value of −4.1 and a Z score value of −2.6. At the left femoral neck level, the T score was −2.9 and the Z score was −1.7; at the right femoral neck level, the T score was −3.1 and the Z score was −1.9. The imaging examination is shown in Figure We recommended that the patient undergo right-sided THA. The patient consented and underwent right THA through a posterolateral approach under general anesthesia. During surgery, we found sclerosis, steatosis and necrosis at the fracture end of the right femoral neck. Two days after surgery, the patient was asked to exercise his quadriceps and calf muscles in bed and use a walker to move with the assistance of a sports therapist. Meanwhile, postoperative pelvic radiographs showed that the THA surgery was very successful Fig. . OverallTwo months after the operation, the patient was admitted to the hospital due to pain in the left hip. At the same time, he denied a history of trauma. The left lower limb could not be straightened when lying down. Blood examination and biochemical laboratory values were within normal limits. The imaging examinations are shown in Figure –7 However, we found that these patients are the most accessible in the clinical setting. Therefore, it is crucial to diagnose these patients promptly and to intervene early and correctly. Failure to identify potential occult fractures in a timely manner may result in serious secondary injury to the patient. In this case, the patient had a left-sided tension femoral neck fracture that was not detected at the first visit, leading the patient to undergo THA surgery on the right side alone. Occult fractures that are not effectively treated slowly develop into displaced fractures through daily life.Femoral neck stress fractures are rare injuries. To the best of our knowledge, the literature reports related to femoral neck stress fractures are mostly case reports on soldiers and athletes, and there are few reports on femoral neck stress fractures in elderly patients.,5,28–32 Fatigue fractures may also be secondary to patients with abnormal femoral neck anatomy.,33–35 The other type of fracture is an insufficiency fracture, in which the quality of the bone is reduced for various reasons, such as senile osteoporosis, postmenopausal osteoporosis, renal bone disease, vitamin D deficiency and other metabolic bone diseases, cortisol use, neurological sexual anorexia, and the use of certain drugs.,26,36,37 Devas et al proposed the earliest classification method for femoral neck stress fractures, which are divided into 2 types according to whether the fracture end is displaced: Type I, simple compression fracture without displacement; and Type II, compression fracture with displaced fracture ends. Blickenstaff and Morris summarized a new classification method: Type I, no obvious fracture line on X-ray, only localized bone thickening in the femoral neck; Type II, fracture line visible on X-ray through the neck or spur of the femur without displacement of the fracture end; and Type III, completely displaced femoral neck fracture. Fullerton and Snowdy further proposed a new classification based on the fracture mechanism (compression and tension fractures) and fracture displacement: Type I, compression fracture of the medial femoral neck; Type II, tension fracture of the lateral femoral neck; and Type III, completely displaced femoral neck fracture. Early diagnosis of femoral neck stress fractures is actually difficult. There may be only partial osteosclerosis or no positive signs at all on pelvic X-ray. Therefore, further hip computed tomography and magnetic resonance imaging (MRI) scans are required when the patient indicates a history of hip pain without positive signs on the pelvic radiograph. Hip MRI is currently the most sensitive imaging test for diagnosing femoral neck stress fractures. For elderly patients, whole-body BMD is also necessary. We recommend further laboratory tests, such as serum vitamin D, bone metabolism indicators and serum electrolytes, for such patients. Treatment depends on the type of fracture. It is necessary to comprehensively consider the patient’s financial situation, age, physical condition and many other factors to formulate the most appropriate treatment plan for individual patients. In general, compression fractures can first be treated conservatively. However, conservative treatment of tension fractures has a high failure rate, and surgery is often considered after failure. Displaced fractures are recommended for surgical intervention. For younger patients, internal fixation with cannulated screws or sliding hip screw fixation is often the treatment of choice, while hip replacement is the usual option after failure of initial surgical treatment. Importantly, etiological treatment should be initiated promptly after surgical treatment to avoid subsequent fractures. For example, patients with osteoporosis require standardized anti-osteoporosis treatment. In addition, proper postoperative rehabilitation is essential. Some patients with a unilateral femoral neck stress fracture may develop a contralateral femoral neck stress fracture during postoperative rehabilitation due to stress changes.Based on the literature summary, we reviewed the case reports of femoral neck stress fractures and the corresponding treatment plans Table . The eti A complete BMD test at the time of admission can confirm the diagnosis of osteoporosis. The patient was a farmer, and his occupation required him to work in the fields for a long time. Therefore, we can speculate that the 2 together caused the bilateral femoral neck stress fracture in this patient, which can also be considered a type of insufficiency fracture. The patient should have been diagnosed with a left tension femoral neck fracture and a right displaced femoral neck fracture at the first visit. However, due to the lack of attention to the left femoral neck edema and abnormal signals indicated by the right hip MRI during the first treatment, no further MRI examination of the contralateral hip was performed, resulting in the missed diagnosis of left tension femoral neck fracture. Subsequently, during the rehabilitation period after the first THA, the patient was strengthened with muscle and pace exercises, which resulted in greater compression and load on the contralateral hip joint. The final result was that the left tension femoral neck fracture gradually developed into a displaced fracture. In this case, if abnormal edema signals in the left hip had been identified during the first hospitalization, early weight-bearing exercise would have been reduced after the right THA, and only muscle and nonweight-bearing training would have been performed. At the same time, intensifying the anti-osteoporotic therapy in this patient and regularly reviewing the progression of the left femoral neck nondisplaced fracture could have been one way to avoid a catastrophic outcome of the left femoral neck. Therefore, we recommend the following treatment options for patients with femoral neck stress fractures without a history of trauma  in patients with hip fractures without a history of trauma, further investigations on bone metabolism, osteoporosis and hip MRI are required to avoid underdiagnosis of occult fractures. (2) Hip fractures among different ages, fracture types and etiologies require individualized treatment options. In elderly patients, we recommend hip replacement to avoid the possibility of failure after internal fixation and the need for reoperation. (3) It is necessary to carry out corresponding basic treatment and regular review according to the etiology of patients with femoral neck stress fractures after surgery.For patients who complain of hip pain but deny a history of trauma, we should be concerned about the presence of a hip fracture even if the patient’s radiograph does not report a positive result. The most sensitive method is bilateral MRI examination of the hip. We recommend a protocol for the management of patients with stress fractures of the femoral neck without a history of trauma. Stress fractures of the femoral neck require early diagnosis and proper treatment, and the necessary etiological treatment is indispensable.Conceptualization: Li Zhang.Investigation: Zhanglu Fang, Jianhua Cao.Visualization: Xun Wang.Writing – original draft: Zhanglu Fang, Xun Wang.Writing – review & editing: Li Zhang.


Radiofrequency echographic multi-spectrometry (REMS) is an ultrasound technique that has been recently introduced in the medical field to detect osteoporosis and fracture risk at axial sites. The use of sonography to visualize the region of interest (ROI) of the hip neck provides the opportunity to identify occult fractures. A 91-year-old woman with persistent right leg pain was referred to rheumatologist due to a known history of arthritis and osteoporosis. She was able to walk using a crutch, although experiencing an antalgic gait. The patient had recently fallen on her right side from standing height. During the visualization of the ROI of the right femoral neck using REMS, an abrupt break of the femoral cortex suspected to be a fracture was seen; therefore, the measurement of the femoral neck was performed on the left side. The T-score had value of −2.9 SD and the fragility score was 86.7. Due to unclear signs of a fracture after an X-ray of the hip, a computed tomography (CT) exam of the hip was performed, which revealed a femoral neck fracture. Occult fractures of the femoral neck are challenging to diagnose and require numerous radiologic exams. The use of ultrasound as a method to measure bone density allows the simultaneous diagnosis of osteoporosis and detection of fractures.