Prediction tools are instruments which are commonly used to estimate the prognosis in oncology and facilitate clinical decision-making in a more personalized manner. Their popularity is shown by the increasing numbers of prediction tools, which have been described in the medical literature. Many of these tools have been shown to be useful in the field of soft-tissue sarcoma of the extremities (eSTS). In this annotation, we aim to provide an overview of the available prediction tools for eSTS, provide an approach for clinicians to evaluate the performance and usefulness of the available tools for their own patients, and discuss their possible applications in the management of patients with an eSTS. Cite this article:
To determine the major risk factors for unplanned reoperations (UROs) following corrective surgery for adult spinal deformity (ASD) and their interactions, using machine learning-based prediction algorithms and game theory. Patients who underwent surgery for ASD, with a minimum of two-year follow-up, were retrospectively reviewed. In total, 210 patients were included and randomly allocated into training (70% of the sample size) and test (the remaining 30%) sets to develop the machine learning algorithm. Risk factors were included in the analysis, along with clinical characteristics and parameters acquired through diagnostic radiology.Aims
Methods
A pilon fracture is a severe ankle joint injury caused by high-energy trauma, typically affecting men of working age. Although relatively uncommon (5% to 7% of all tibial fractures), this injury causes among the worst functional and health outcomes of any skeletal injury, with a high risk of serious complications and long-term disability, and with devastating consequences on patients’ quality of life and financial prospects. Robust evidence to guide treatment is currently lacking. This study aims to evaluate the clinical and cost-effectiveness of two surgical interventions that are most commonly used to treat pilon fractures. A randomized controlled trial (RCT) of 334 adult patients diagnosed with a closed type C pilon fracture will be conducted. Internal locking plate fixation will be compared with external frame fixation. The primary outcome and endpoint will be the Disability Rating Index (a patient self-reported assessment of physical disability) at 12 months. This will also be measured at baseline, three, six, and 24 months after randomization. Secondary outcomes include the Olerud and Molander Ankle Score (OMAS), the five-level EuroQol five-dimenison score (EQ-5D-5L), complications (including bone healing), resource use, work impact, and patient treatment preference. The acceptability of the treatments and study design to patients and health care professionals will be explored through qualitative methods.Aims
Methods
The use of technology to assess balance and alignment during total knee surgery can provide an overload of numerical data to the surgeon. Meanwhile, this quantification holds the potential to clarify and guide the surgeon through the surgical decision process when selecting the appropriate bone recut or soft tissue adjustment when balancing a total knee. Therefore, this paper evaluates the potential of deploying supervised machine learning (ML) models to select a surgical correction based on patient-specific intra-operative assessments. Based on a clinical series of 479 primary total knees and 1,305 associated surgical decisions, various ML models were developed. These models identified the indicated surgical decision based on available, intra-operative alignment, and tibiofemoral load data.Aims
Methods
Heterotopic ossification (HO) is perhaps the
single most significant obstacle to independence, functional mobility, and
return to duty for combat-injured veterans of Operation Enduring
Freedom and Operation Iraqi Freedom. Recent research into the cause(s)
of HO has been driven by a markedly higher prevalence seen in these
wounded warriors than encountered in previous wars or following
civilian trauma. To that end, research in both civilian and military
laboratories continues to shed light onto the complex mechanisms
behind HO formation, including systemic and wound specific factors,
cell lineage, and neurogenic inflammation. Of particular interest,
non-invasive
