Aims. To develop and internally validate a preoperative
To investigate whether pre-operative functional mobility is a
determinant of delayed inpatient recovery of activities (IRoA) after
total knee arthroplasty (TKA) in three periods that coincided with
changes in the clinical pathway. All patients (n = 682, 73% women, mean age 70 years, standard
deviation 9) scheduled for TKA between 2009 and 2015 were pre-operatively
screened for functional mobility by the Timed-up-and-Go test (TUG)
and De Morton mobility index (DEMMI). The cut-off point for delayed
IRoA was set on the day that 70% of the patients were recovered,
according to the Modified Iowa Levels of Assistance Scale (mILAS)
(a 5-item activity scale). In a multivariable logistic regression
analysis, we added either the TUG or the DEMMI to a reference model
including established determinants.Aims
Patients and Methods
Acute bone and joint infections in children are serious, and misdiagnosis can threaten limb and life. Most young children who present acutely with pain, limping, and/or loss of function have transient synovitis, which will resolve spontaneously within a few days. A minority will have a bone or joint infection. Clinicians are faced with a diagnostic challenge: children with transient synovitis can safely be sent home, but children with bone and joint infection require urgent treatment to avoid complications. Clinicians often respond to this challenge by using a series of rudimentary decision support tools, based on clinical, haematological, and biochemical parameters, to differentiate childhood osteoarticular infection from other diagnoses. However, these tools were developed without methodological expertise in diagnostic accuracy and do not consider the importance of imaging (ultrasound scan and MRI). There is wide variation in clinical practice with regard to the indications, choice, sequence, and timing of imaging. This variation is most likely due to the lack of evidence concerning the role of imaging in acute bone and joint infection in children. We describe the first steps of a large UK multicentre study, funded by the National Institute for Health Research, which seeks to integrate definitively the role of imaging into a decision support tool, developed with the assistance of individuals with expertise in the development of
Aims. The risk factors for recurrent instability (RI) following a primary traumatic anterior shoulder dislocation (PTASD) remain unclear. In this study, we aimed to determine the rate of RI in a large cohort of patients managed nonoperatively after PTASD and to develop a
A prospective study was performed to develop
a
A suspected fracture of the scaphoid remains difficult to manage despite advances in knowledge and imaging methods. Immobilisation and restriction of activities in a young and active patient must be balanced against the risks of nonunion associated with an undiagnosed and undertreated fracture of the scaphoid. The assessment of diagnostic tests for a suspected fracture of the scaphoid must take into account two important factors. First, the prevalence of true fractures among suspected fractures is low, which greatly reduces the probability that a positive test will correspond with a true fracture, as false positives are nearly as common as true positives. This situation is accounted for by Bayesian statistics. Secondly, there is no agreed reference standard for a true fracture, which necessitates the need for an alternative method of calculating diagnostic performance characteristics, based upon a statistical method which identifies clinical factors tending to associate (latent classes) in patients with a high probability of fracture. The most successful diagnostic test to date is MRI, but in low-prevalence situations the positive predictive value of MRI is only 88%, and new data have documented the potential for false positive scans. The best strategy for improving the diagnosis of true fractures among suspected fractures of the scaphoid may well be to develop a
The aim of this study was to describe the introduction of a virtual pathway for the management of patients with a suspected fracture of the scaphoid, and to report patient-reported outcome measures (PROMs) and satisfaction following treatment using this service. All adult patients who presented with a clinically suspected scaphoid fracture that was not visible on radiographs at the time of presentation during a one-year period were eligible for inclusion in the pathway. Demographic details, findings on examination, and routine four-view radiographs at the time of presentation were collected. All radiographs were reviewed virtually by a single consultant hand surgeon, with patient-initiated follow-up on request. PROMs were assessed at a minimum of one year after presentation and included the abbreviated version of the Disabilities of the Arm, Shoulder and Hand Score (QuickDASH), the EuroQol five-dimension five-level health questionnaire (EQ-5D-5L), the Net Promoter Score (NPS), and return to work.Aims
Methods
To examine whether natural language processing (NLP) using a clinically based large language model (LLM) could be used to predict patient selection for total hip or total knee arthroplasty (THA/TKA) from routinely available free-text radiology reports. Data pre-processing and analyses were conducted according to the Artificial intelligence to Revolutionize the patient Care pathway in Hip and knEe aRthroplastY (ARCHERY) project protocol. This included use of de-identified Scottish regional clinical data of patients referred for consideration of THA/TKA, held in a secure data environment designed for artificial intelligence (AI) inference. Only preoperative radiology reports were included. NLP algorithms were based on the freely available GatorTron model, a LLM trained on over 82 billion words of de-identified clinical text. Two inference tasks were performed: assessment after model-fine tuning (50 Epochs and three cycles of k-fold cross validation), and external validation.Aims
Methods
There is increasing popularity in the use of artificial intelligence and machine-learning techniques to provide diagnostic and prognostic models for various aspects of Trauma & Orthopaedic surgery. However, correct interpretation of these models is difficult for those without specific knowledge of computing or health data science methodology. Lack of current reporting standards leads to the potential for significant heterogeneity in the design and quality of published studies. We provide an overview of machine-learning techniques for the lay individual, including key terminology and best practice reporting guidelines. Cite this article:
The aim of this study was to evaluate the diagnostic value of preoperative serum CRP, white blood cell count (WBC), percentage of neutrophils (%N), and neutrophil to lymphocyte ratio (NLR) when using the fracture-related infection (FRI) consensus definition. A cohort of 106 patients having surgery for suspected septic nonunion after failed fracture fixation were studied. Blood samples were collected preoperatively, and the concentration of serum CRP, WBC, and differential cell count were analyzed. The areas under the curve (AUCs) of diagnostic tests were compared using the z-test. Regression trees were constructed and internally cross-validated to derive a simple diagnostic decision tree.Aims
Methods
Previously, we showed that case-specific non-linear
finite element (FE) models are better at predicting the load to failure
of metastatic femora than experienced clinicians. In this study
we improved our FE modelling and increased the number of femora
and characteristics of the lesions. We retested the robustness of
the FE predictions and assessed why clinicians have difficulty in
estimating the load to failure of metastatic femora. A total of
20 femora with and without artificial metastases were mechanically
loaded until failure. These experiments were simulated using case-specific
FE models. Six clinicians ranked the femora on load to failure and
reported their ranking strategies. The experimental load to failure
for intact and metastatic femora was well predicted by the FE models (R2 =
0.90 and R2 = 0.93, respectively). Ranking metastatic
femora on load to failure was well performed by the FE models (τ =
0.87), but not by the clinicians (0.11 <
τ <
0.42). Both the
FE models and the clinicians allowed for the characteristics of
the lesions, but only the FE models incorporated the initial bone
strength, which is essential for accurately predicting the risk
of fracture. Accurate prediction of the risk of fracture should
be made possible for clinicians by further developing FE models.
The crucial differentiation between septic arthritis and transient synovitis of the hip in children can be difficult. In 1999, Kocher et al introduced four clinical predictors which were highly predictive (99.6%) of septic arthritis. These included fever (temperature ≥ 38.5°C), inability to bear weight, white blood-cell count >
12.0 × 109 cells/L and ESR ≥ 40 mm/hr; CRP ≥ 20 mg/L was later added as a fifth predictor. We retrospectively evaluated these predictors to differentiate septic arthritis from transient synovitis of the hip in children over a four-year period in a primary referral general hospital. When all five were positive, the predicted probability of septic arthritis in this study was only 59.9%, with fever being the best predictor. When applied to low-prevalence diseases, even highly specific tests yield a high number of false positives and the predictive value is thereby diminished. Clinical predictors should be applied with caution when assessing a child with an irritable hip, and a high index of suspicion, and close observation of patients at risk should be maintained.