OBJECTIVES:Intramedullary nailing is the treatment of choice for most tibial shaft fractures (TSF). However, an iatrogenic pitfall may be rotational malalignment. The aim of this retrospective analysis was to determine predictors of rotational malalignment following intramedullary nailing of TSF. METHODS: DESIGN:Retrospective study. SETTING:Single level 1 trauma center. PATIENT SELECTION CRITERIA:Patients who had a unilateral intramedullary nailing for TSF with a low-dose bilateral postoperative CT to assess rotational malalignment. OUTCOME MEASURES AND COMPARISONS:Bivariable analysis followed by multivariable analysis was then undertaken to assess for any independent predictors, such as fracture type/sight, surgeon experience, and side of fracture, predictive of rotational malalignment. RESULTS:In total, 154 patients (71% male, median age 37 years) were included in this study. Thirty-nine percent of variability in postoperative rotational malalignment could be explained using a model including (increased) tibial torsion of the noninjured side (mean [38.9 degrees ± 9.02 degrees] considered normal tibial torsion), side of tibial fracture, and spiral-type tibial fracture (R2 = 0.39, P ≤ 0.001, F = 31.40). In this model, there was a negative linear association between degrees of torsion on the noninjured side and rotational malalignment (-0.45, P < 0.001)-as baseline torsion increased from mean by 1 degree, malrotation in the opposite direction of 0.54 degrees seen. Positive linear associations between right-sided TSF and rotational malalignment (8.59 P < 0.001) as well as spiral fractures and rotational malalignment (5.03, P < 0.01) were seen. CONCLUSIONS:This study demonstrates that baseline reduced (internal) tibial torsion of the noninjured limb, spiral fractures, and right-sided TSF are predictive of postoperative external rotational malalignment. Conversely, increased baseline (external) tibial torsion of the noninjured limb and left-sided TSF are predictive of postoperative internal rotational malalignment. Surgeons may use this regression model preoperatively to predict what sort of postoperative rotational difference their patient may be prone to. LEVEL OF EVIDENCE:Prognostic Level III. See Instructions for Authors for a complete description of levels of evidence.

Background:Up to 30% of patients with a tibial shaft fracture sustain iatrogenic rotational malalignment (RM) after infrapatellar (IP) nailing. Although IP nailing remains the management of choice for most patients, suprapatellar (SP) nailing has been gaining popularity. It is currently unknown whether SP nailing can provide superior outcomes with regard to tibial RM. The aim of this study was to compare the differences in the prevalence of RM following IP versus SP nailing. Methods:This retrospective study included 253 patients with a unilateral, closed tibial shaft fracture treated with either an IP or SP approach between January 2009 and April 2023 in a Level-I trauma center. All patients underwent a postoperative, protocolized, bilateral computed tomography (CT) scan for RM assessment. Results:RM was observed in 30% and 33% of patients treated with IP and SP nailing, respectively. These results indicate no significant difference (p = 0.639) in the prevalence of RM between approaches. Furthermore, there were no significant differences in the distribution (p = 0.553) and direction of RM (p = 0.771) between the 2 approaches. With the IP and SP approaches, nailing of left-sided tibial shaft fractures resulted in predominantly internal RM (85% and 73%, respectively), while nailing of right-sided tibial shaft fractures resulted in predominantly external RM (90% and 80%, respectively). The intraobserver reliability for the CT measurements was 0.95. Conclusions:The prevalence of RM was not influenced by the entry point of tibial nailing (i.e., IP versus SP). Hence, the choice of surgical approach should rely on factors other than the risk of RM. Level of Evidence:Therapeutic Level III. See Instructions for Authors for a complete description of levels of evidence.

Purpose: The aim of this study was to develop and train a machine learning(ML) algorithm to create a clinical decision support tool (i.e., ML-driven probability calculator) to be used in clinical practice to estimate recurrence rates following an arthroscopic Bankart repair (ABR). Methods: Data from 14 previously published studies were collected. Inclusion criteria were (1) patients treated with ABR without remplissage for traumatic anterior shoulder instability and (2) a minimum of 2 years follow-up. Risk factors associated with recurrence were identified using bivariate logistic regression analysis. Subsequently, four ML algorithms were developed and internally validated. The predictive performance was assessed using discrimination, calibration and the Brier score. Results: In total, 5591 patients underwent ABR with a recurrence rate of15.4% (n= 862). Age <35 years, participation in contact and collision sports, bony Bankart lesions and full-thickness rotator cuff tears increased the risk of recurrence (all p< 0.05). A single shoulder dislocation (compared to multiple dislocations) lowered the risk of recurrence (p< 0.05). Due to the unavailability of certain variables in some patients, a portion of the patient data had to be excluded before pooling the data set to create the algorithm. A total of 797 patients were included providing information on risk factors associated with recurrence. The discrimination (area under the receiver operating curve)ranged between 0.54 and 0.57 for prediction of recurrence. Conclusion: ML was not able to predict the recurrence following ABR with the current available predictors. Despite a global coordinated effort, the heterogeneity of clinical data limited the predictive capabilities of the algorithm, emphasizing the need for standardized data collection methods in future studies.

IntroductionThe effectiveness of rotator cuff tear repair surgery is influenced by multiple patient-related, pathology-centred and technical factors, which is thought to contribute to the reported retear rates between 17% and 94%. Adequate patient selection is thought to be essential in reaching satisfactory results. However, no clear consensus has been reached on which factors are most predictive of successful surgery. A clinical decision tool that encompassed all aspects is still to be made. Artificial intelligence (AI) and machine learning algorithms use complex self-learning models that can be used to make patient-specific decision-making tools. The aim of this study is to develop and train an algorithm that can be used as an online available clinical prediction tool, to predict the risk of retear in patients undergoing rotator cuff repair.Methods and analysisThis is a retrospective, multicentre, cohort study using pooled individual patient data from multiple studies of patients who have undergone rotator cuff repair and were evaluated by advanced imaging for healing at a minimum of 6 months after surgery. This study consists of two parts. Part one: collecting all potential factors that might influence retear risks from retrospective multicentre data, aiming to include more than 1000 patients worldwide. Part two: combining all influencing factors into a model that can clinically be used as a prediction tool using machine learning.Ethics and disseminationFor safe multicentre data exchange and analysis, our Machine Learning Consortium adheres to the WHO regulation 'Policy on Use and Sharing of Data Collected by WHO in Member States Outside the Context of Public Health Emergencies'. The study results will be disseminated through publication in a peer-reviewed journal. Institutional Review Board approval does not apply to the current study protocol.

Introduction Shoulder instability is a common injury, with a reported incidence of 23.9 per 100000 person-years. There is still an ongoing debate on the most effective treatment strategy. Non-operative treatment has recurrence rates of up to 60%, whereas operative treatments such as the Bankart repair and bone block procedures show lower recurrence rates (16% and 2%, respectively) but higher complication rates (<2%and up to 30%, respectively). Methods to determine risk of recurrence have been developed; however, patient-specific decision-making tools are still lacking. Artificial intelligence and machine learning algorithms use self-learning complex models that can be used to make patient-specific decision-making tools. The aim of the current study is to develop and train a machine learning algorithm to create a prediction model to be used in clinical practice-as an online prediction tool-to estimate recurrence rates following a Bankart repair. Methods and analysis This is a multicentre retrospective cohort study. Patients with traumatic anterior shoulder dislocations that were treated with an arthroscopic Bankart repair without remplissage will be included. This study includes two parts. Part 1, collecting all potential factors influencing the recurrence rate following an arthroscopic Bankart repair in patients using multicentre data, aiming to include data from >1000 patients worldwide. Part 2, the multicentre data will be re-evaluated (and where applicable complemented) using machine learning algorithms to predict outcomes. Recurrence will be the primary outcome measure. Ethics and dissemination For safe multicentre data exchange and analysis, our Machine Learning Consortium adhered to the WHO regulation 'Policy on Use and Sharing of Data Collected by WHO in Member States Outside the Context of Public Health Emergencies'. The study results will be disseminated through publication in a peer-reviewed journal. No Institutional Review Board is required for this study.

Background Femoral neck fractures are common and are frequently treated with internal fixation. A major disadvantage of internal fixation is the substantially high number of conversions to arthroplasty because of nonunion, malunion, avascular necrosis, or implant failure. A clinical prediction model identifying patients at high risk of conversion to arthroplasty may help clinicians in selecting patients who could have benefited from arthroplasty initially. Question/purpose What is the predictive performance of a machine-learning (ML) algorithm to predict conversion to arthroplasty within 24 months after internal fixation in patients with femoral neck fractures? MethodsWe included 875 patients from the Fixation using Alternative Implants for the Treatment of Hip fractures (FAITH) trial. The FAITH trial consisted of patients with low-energy femoral neck fractures who were randomly assigned to receive a sliding hip screw or cancellous screws for internal fixation. Of these patients, 18% (155 of 875) underwent conversion to THA or hemiarthroplasty within the first 24 months. All patients were randomly divided into a training set (80%) and test set (20%). First, we identified 27 potential patient and fracture characteristics that may have been associated with our primary outcome, based on biomechanical rationale and previous studies. Then, random forest algorithms (an ML learning, decision tree-based algorithm that selects variables) identified 10 predictors of conversion: BMI, cardiac disease, Garden classification, use of cardiac medication, use of pulmonary medication, age, lung disease, osteoarthritis, sex, and the level of the fracture line. Based on these variables, five different ML algorithms were trained to identify patterns related to conversion. The predictive performance of these trained ML algorithms was assessed on the training and test sets based on the following performance measures: (1) discrimination (the model's ability to distinguish patients who had conversion from those who did not; expressed with the area under the receiver operating characteristic curve [AUC]), (2) calibration (the plotted estimated versus the observed probabilities; expressed with the calibration curve intercept and slope), and (3) the overall model performance (Brier score: a composite of discrimination and calibration). Results None of the five ML algorithms performed well in predicting conversion to arthroplasty in the training set and the test set; AUCs of the algorithms in the training set ranged from 0.57 to 0.64, slopes of calibration plots ranged from 0.53 to 0.82, calibration intercepts ranged from -0.04 to 0.05, and Brier scores ranged from 0.14 to 0.15. The algorithms were further evaluated in the test set; AUCs ranged from 0.49 to 0.73, calibration slopes ranged from 0.17 to 1.29, calibration intercepts ranged from -1.28 to 0.34, and Brier scores ranged from 0.13 to 0.15. Conclusion The predictive performance of the trained algorithms was poor, despite the use of one of the best datasets available worldwide on this subject. If the current dataset consisted of different variables or more patients, the performance may have been better. Also, various reasons for conversion to arthroplasty were pooled in this study, but the separate prediction of underlying pathology (such as, avascular necrosis or nonunion) may be more precise. Finally, it may be possible that it is inherently difficult to predict conversion to arthroplasty based on preoperative variables alone.Therefore, future studies should aim to include more variables and to differentiate between the various reasons for arthroplasty. Level of Evidence Level III, prognostic study.

Background: Risk stratification of individual patients who are prone to infection would allow surgeons to monitor high-risk patients more closely and intervene early when needed. This could reduce infection-related consequences such as increased health-care costs. The purpose of this study was to develop a machine learning (ML)-derived risk-stratification tool using the SPRINT (Study to Prospectively Evaluate Reamed Intramedullary Nails in Patients with Tibial Fractures) and FLOW (Fluid Lavage of Open Wounds) trial databases to estimate the probability of infection in patients with operatively treated tibial shaft fractures (TSFs). Methods: Patients with unilateral TSFs from the SPRINT and FLOW trials were randomly split into derivation (80%) and validation (20%) cohorts. Random forest algorithms were used to select features that are relevant to predicting infection. These features were included for algorithm training. Five ML algorithms were trained in recognizing patterns associated with infection. The performance of each ML algorithm was evaluated and compared based on (1) the area under the ROC (receiver operating characteristic) curve (AUC), (2) the calibration slope and the intercept, and (3) the Brier score. Results: There were 1,822 patients included in this study: 170 patients (9%) developed an infection that required treatment, 62 patients (3%) received nonoperative treatment with oral or intravenous antibiotics, and 108 patients (6%) underwent subsequent surgery in addition to antibiotic therapy. Random forest algorithms identified 7 variables that were relevant for predicting infection: (1) Gustilo-Anderson or Tscherne classification, (2) bone loss, (3) mechanism of injury, (4) multitrauma, (5) AO/OTA fracture classification, (6) age, and (7) fracture location. Training of the penalized logistic regression algorithm resulted in the best-performing prediction model, with AUC, calibration slope, calibration intercept, and Brier scores of 0.75, 0.94, 0.00, and 0.076, respectively, in the derivation cohort and 0.81, 1.07, 0.09, and 0.079, respectively, in the validation cohort. Conclusions: We developed an ML prediction model that can estimate the probability of infection for individual patients with TSFs based on patient and fracture characteristics that are readily available at hospital admission.

Purpose: The decision to continue immobilization of a nondisplaced scaphoid waist fracture is often based on radiographic appearance (despite evidence that radiographs are unreliable and inaccurate for diagnosing scaphoid union 6-12 weeks after fracture) and fracture tenderness (even though it is influenced by cognitive biases on pain). This may result in unhelpful additional immobilization. We studied nondisplaced scaphoid waist fractures to determine the factors associated with (1) the surgeon's decision to continue cast or splint immobilization at the first visit when cast removal was being considered; (2) greater pain on examination; and (3) the surgeon's concern about radiographic consolidation. Methods: We prospectively included 46 patients with a nondisplaced scaphoid waist fracture treated nonoperatively. At the first visit when cast removal was considered - after an average of 6 weeks of immobilization - patients rated pain during 4 examination maneuvers. The treating surgeon assessed union on radiographs and decided whether to continue or discontinue immobilization. Patients completed measures of the following: (1) the degree to which pain limits activities (Patient-Reported Outcome Measure Interactive System [PROMIS] Pain Interference Computer Adaptive Test [CAT], Pain Self-Efficacy Questionnaire-2); (2) symptoms of depression (PROMIS Depression CAT); and (3) upper extremity function (PROMIS Upper Extremity Function CAT). We used multivariable regression analysis to investigate the factors associated with each outcome. Results: Perceived inadequate radiographic healing and greater symptoms of depression were independently associated with continued immobilization. Pain during the examination was not associated with continued immobilization. Patient age was associated with pain on examination. Shorter immobilization duration was the only factor associated with the surgeon's perception of inadequate radiographic consolidation. Conclusions: Inadequate radiographic healing and greater symptoms of depression are associated with a surgeon's decision to continue cast or splint immobilization of a nondisplaced scaphoid waist fracture. Copyright (C) 2021 by the American Society for Surgery of the Hand. All rights reserved.

Objectives: To assess the effectiveness of suprapatellar (SP)-nailing versus infrapatellar (IP)-nailing of tibia fractures in anterior knee pain, complications (retropatellar chondropathy, infection, and malalignment) and physical functioning and quality of life. A clinical question-driven and thorough systematic review of current literature is provided. Data source: PubMed and Embase databases were searched for studies published between 2010 and 2020 relating to SP and IP-nailing of tibia fractures. The study is performed in concordance with PRISMA-guidelines. Study selection: Studies eligible for inclusion were randomized controlled trials, prospective and retrospective observational studies reporting on outcomes of interest. Data extraction: Data extraction was performed independently by 2 assessors. Methodological quality and risk of bias was assessed according to the guidelines of the McMaster Critical Appraisal. Data synthesis: Continuous variables are presented as means with SD and dichotomous variables as frequency and percentages. The weighted mean, standardized weighted mean differences, and 95% confidence interval were calculated. A pooled analysis could not be performed because of differences in outcome measures, time-points, and heterogeneity. Results: Fourteen studies with 1447 patients were analyzed. The weighted incidence of anterior knee pain was 29% after SP-nailing and 39% after IP-nailing, without reported significance. There was a significant lower rate of malalignment after the SP-approach (4% vs. 26%) with small absolute differences in all planes. No substantial differences were observed in retropatellar chondropathy, infection, physical functioning, and quality of life. Conclusions: This systematic review does not reveal superiority of either technique in any of the respective outcomes of interest. Definitive choice should depend on the surgeon's experience and available resources. Level of Evidence: Therapeutic Level II. See Instructions for Authors for a complete description of levels of evidence.

Objectives: In the SPRINT trial, 18% of patients with a tibial shaft fracture (TSF) treated with intramedullary nailing (IMN) had one or more unplanned subsequent surgical procedures. It is clinically relevant for surgeon and patient to anticipate unplanned secondary procedures, other than operations that can be readily expected such as reconstructive procedures for soft tissue defects. Therefore, the objective of this study was to develop a machine learning (ML) prediction model using the SPRINT data that can give individual patients and their care team an estimate of their particular probability of an unplanned second surgery. Methods: Patients from the SPRINT trial with unilateral TSFs were randomly divided into a training set (80%) and test set (20%). Five ML algorithms were trained in recognizing patterns associated with subsequent surgery in the training set based on a subset of variables identified by random forest algorithms. Performance of each ML algorithm was evaluated and compared based on (1) area under the ROC curve, (2) calibration slope and intercept, and (3) the Brier score. Results: Total data set comprised 1198 patients, of whom 214 patients (18%) underwent subsequent surgery. Seven variables were used to train ML algorithms: (1) Gustilo-Anderson classification, (2) Tscherne classification, (3) fracture location, (4) fracture gap, (5) polytrauma, (6) injury mechanism, and (7) OTA/AO classification. The best-performing ML algorithm had an area under the ROC curve, calibration slope, calibration intercept, and the Brier score of 0.766, 0.954, -0.002, and 0.120 in the training set and 0.773, 0.922, 0, and 0.119 in the test set, respectively. Conclusions: An ML algorithm was developed to predict the probability of subsequent surgery after IMN for TSFs. This ML algorithm may assist surgeons to inform patients about the probability of subsequent surgery and might help to identify patients who need a different perioperative plan or a more intensive approach.

Background: Intramedullary (IM) nailing is the treatment of choice for most tibial shaft fractures. However, an iatrogenic pitfall may be rotational malalignment. The aims of this retrospective analysis were to determine (1) the prevalence of rotational malalignment using postoperative computed tomography (CT) as the reference standard; (2) the average baseline tibial torsion of uninjured limbs; and (3) based on that normal torsion, whether the contralateral, uninjured limb can be reliably used as the reference standard. Methods: The study included 154 patients (71% male and 29% female) with a median age of 37 years. All patients were treated for a unilateral tibial shaft fracture with an IM nail and underwent low-dose bilateral postoperative CT to assess rotational malalignment. Results: More than one-third of the patients (n = 55; 36%) had postoperative rotational malalignment of ≥10°. Right-sided tibial shaft fractures were significantly more likely to display external rotational malalignment whereas left-sided fractures were predisposed to internal rotational malalignment. The uninjured right tibiae were an average of 4° more externally rotated than the left (mean rotation and standard deviation, 41.1° ± 8.0° [right] versus 37.0° ± 8.2° [left]; p < 0.01). Applying this 4° correction to our cohort not only reduced the prevalence of rotational malalignment (n = 45; 29%), it also equalized the distribution of internal and external rotational malalignment between the left and right tibiae. Conclusions: This study confirms a high prevalence of rotational malalignment following IM nailing of tibial shaft fractures (36%). There was a preexisting 4° left-right difference in tibial torsion, which sheds a different light on previous studies and current clinical practice and could have important implications for the diagnosis and management of tibial rotational malalignment. Level of Evidence: Therapeutic Level IV . See Instructions for Authors for a complete description of levels of evidence.

BACKGROUND:Intramedullary nailing of tibial shaft fractures has been common practice for decades. Nevertheless, complications occur frequently, and subsequent surgery is often required. To improve our understanding on how we may improve trauma care for patients with tibial shaft fractures, this study systematically reviewed all currently available evidence to assess the incidence of complications and rate of re-operations following intramedullary nailing of traumatic tibial fractures.METHODS:Trip Database, Medline, Scopus and Cochrane Library were searched on September 7th, 2018. Searches were limited to English studies published after January 1st, 1998. Studies were included if authors included more than 50 patients treated with intramedullary nailing for traumatic tibial fractures. Inclusion of studies and critical appraisal of the evidence was performed by two independent authors. Incidence of complications and rate of re-operations were reported with descriptive statistics.RESULTS:Fifty-one studies involving 8110 patients treated with intramedullary nailing for traumatic tibial fractures were included. Mean age of patients was 37.5 years. The most frequent complication was anterior knee pain (23%), followed by non-union (11%). Eighteen percent of patients required at least one subsequent surgery. The most frequent indication of subsequent surgery was screw removal due to pain or discomfort (9%). Dynamization of the nail to promote union was reported in 8% of the cases. Nail revision and bone-grafting to promote union were applied in 4% and 2% respectively.DISCUSSION & CONCLUSION:Patients treated with intramedullary nailing for tibial fractures need to be consented for high probability of adverse events as anterior knee pain, subsequent surgical procedures and bone healing problems are relatively common. However, based on current data it remains difficult to identify specifiers and determinants of an individual patient with specific fracture characteristics at risk for complications. Future studies should aim to establish patient specific risks models for complications and re-operations, such that clinicians can anticipate them and adjust and individualize treatment strategies.

Objective The objective of this systematic review is to provide an overview of all interpretations of the definition of the watershed line used in literature. Methods The Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines were followed for this review. A comprehensive search was performed for definitions of the watershed line given in literature. A total of 32 studies giving an explicit interpretation of the definition of the watershed line or anatomical reference for plate positioning in writing and/or imaging were included. Results In 32 studies, we found eight different landmarks used to refer to the watershed line or correct plate positioning. Five studies used two different soft tissue landmarks. Six different bony landmarks were described in 24 studies. These could further be subdivided into three anatomical interpretations, described in seven studies, in which the term "watershed line" is explained as a distinguishable anatomical line, and two surgical interpretations, described in 15 studies, which are purely reflecting the optimal location of the volar plate. One interpretation of the watershed line described in two studies combined both anatomical and surgical landmarks. Conclusion The (mis)interpretation of the definition of the term "watershed line" as described by Orbay is subject to the type of landmarks and purpose used: soft tissue or bony landmarks and an anatomical or a surgical purpose. A clear distinction can be made between interpretations using bony landmarks, as the true watershed line is defined and definitions using soft tissue landmarks, which might represent the reference points surgeons use in clinical practice.

OBJECTIVES:To develop an accurate machine learning (ML) predictive model incorporating patient, fracture, and trauma characteristics to identify individual patients at risk of an (occult) PMF.METHODS:Databases of 2 studies including patients with TSFs from 2 Level 1 trauma centers were combined for analysis. Using ten-fold cross-validation, 4 supervised ML algorithms were trained in recognizing patterns associated with PMFs: (1) Bayes point machine; (2) support vector machine; (3) neural network; and (4) boosted decision tree. Performance of each ML algorithm was evaluated and compared based on (1) C-statistic; (2) calibration slope and intercept; and (3) Brier score. The best-performing ML algorithm was incorporated into an online open-access prediction tool.RESULTS:Total data set included 263 patients, of which 28% had a PMF. Training of the Bayes point machine resulted in the best-performing prediction model reflected by good C-statistic, calibration slope, calibration intercept, and Brier score of 0.89, 1.02, -0.06, and 0.106, respectively. This prediction model was deployed as an open-access online prediction tool.CONCLUSION:A ML-based prediction model accurately predicted the probability of a (occult) PMF in patients with a TSF based on patient- and fracture-specific characteristics. This prediction model can guide surgeons in their diagnostic workup and preoperative planning. Further research is required to externally validate the model before implementation in clinical practice.LEVEL OF EVIDENCE:Prognostic Level III. See Instructions for Authors for a complete description of levels of evidence.

Objectives: To (1) evaluate the incidence of posterior malleolar fractures (PMFs) in patients with tibial shaft fractures (TSFs) using advanced imaging; (2) identify predictors for patients at risk of an (occult) PMF; and (3) describe PMF characteristics to guide “malleolus-first” fixation. Design: Retrospective diagnostic imaging study. Setting: Level-I trauma center. Patients: One hundred sixty-four patients treated with intramedullary nailing for TSFs who underwent low-dose postoperative computed tomography (CT) scans to assess (mal)rotational alignment. Intervention: Analysis of advanced imaging for the presence of PMFs. Univariate and multivariate analyses to identify predictors. Qualitative analysis of PMFs by fracture mapping. Main Outcome Measures: (1) Incidence of PMFs in patients with TSFs as diagnosed on post-op CT scans; (2) independent predictors for the presence of PMFs; and (3) PMF patterns. Results: One in five patients with a TSF has an associated PMF (22%), increasing to one-in-two in patients with simple spiral fractures (56%). In 25% of patients, these fractures were occult. Univariate analysis identified simple spiral and distal third TSFs, proximal third and spiral fibula fractures, and low-energy trauma as predictors for PMFs. Multivariate analysis demonstrated that distal third and simple spiral TSFs were the only independent predictors. Haraguchi type I is the pattern specific to PMFs associated with TSF. Conclusions: Half of patients presenting with a simple spiral TSF have an associated PMF. In one in four patients, these are occult. Additional preoperative CT scan imaging may be considered in patients presenting with simple spiral distal third TSFs, despite negative lateral radiographs, so that PMFs can be identified and managed with “malleolus-first” fixation. Level of Evidence: Diagnostic Level IV. See Instructions for Authors for a complete description of levels of evidence.