ObjectiveTo introduce and interpret ABCD classification system for subaxial cervical spine injury.
MethodsThe literature related to subaxial cervical spine injury classification system was extensively reviewed, analyzed, and summarized so as to introduce the ABCD classification system.
ResultsThe ABCD classification system for subaxial cervical spine injury consists of 3 parts. The first part of the proposed classification is an anatomical descri ption of the injury; it del ivers the information whether injury is bony, ligamentous, or a combined one. The second part is the classification of nerve function, spinal stenosis, and spinal instabil ity. The last part is optional and denotes radiological examination which is used to define injury type. Several letters have been used for simplicity to del iver the largest amount of information. And a treatment algorithm based on the proposed classification is suggested.
ConclusionThe ABCD classification system is proposed for simplification. However further evaluation of this classification is needed.
One hundred and thirty patients with uveitis in north-western zone of our country were analyzed based on anatomical classification and their causes. It was found that anterior uveitis was the commonest type in uveitis,accounting for 86.15% of total patients. Intermediate uveitis, pan-uveitis and posterior uveitis accounted repectively for 6.92%, 3.85%and3.08% of the total patients. Rheumatic arthritis was the most frequently accompanied systemic disease in patients with uveitis,showing a possibly causative link between them in their pathogenesis.
（Chin J Ocul Fundus Dis,1994,10:156-158）
ObjectiveTo evaluate the differences of visual evoked potentials (amplitudes and latency) between cerebral palsy (CP) children and normal children.
MethodsThis study involved fourteen children aged from 4 to 7 years with CP (monoplegia) between 2009 and 2013. Another 14 normal children aged from 5 to 9 years treated in the Department of Ophthalmology in West China Hospital during the same period were regarded as the control group. Both eyes of all the participants were examined by multifocal visual evoked potential (mfVEP). The mfVEP examination results were recorded, and amplitude and latency were analyzed. First, we analyzed the differences of amplitudes and latency time between monoplegia children and children in the control group. Second, gross motor function classification system (GMFCS) was used to classify the fourteen monoplegia children among whom there were five GMFCS Ⅰ patients and nine GMFCS Ⅱ patients. The differences of mfVEP were analyzed between the two GMFCS groups.
ResultsThe amplitude and latency of mfVEP in children with CP showed gradual changes similar to those in the normal children. The amplitudes were decreasing and the latencies were delaying from the first eccentricity to the sixth eccentricity. The amplitudes in children with CP were lower than those in the control group in the first to the third eccentricities for both eyes (P＜0.05), and latency of left eye was delayed in the first eccentricity in children with CP (P=0.045). No difference was found between the two GMFCS groups (P>0.05) except the amplitude of the first eccentricity (P=0.043).
ConclusionsThe results of mfVEP show significant differences of amplitude and latency between CP and normal children, suggesting the existence of visual pathway impairments in cerebral palsy children. The results of mfVEP can provide an objective basis of visual impairments for cerebral palsy children.
Objective To summary the pathological classification， clinical manifestations， diagnosis， and treatments of gastrointestinal neuroendocrine tumors （GE-NETs）. Methods Domestic and international literatures were collected to summary the status of clinical researches and treatments of GE-NETs. Results GE-NETs derived from enterochromaffin cells throughout the gut which had the function of amine precursor uptake decarboxylase （APUD）. These tumors secreted discrete bioactive substances and produced characteristic immunohistochemical patterns， making patients to manifest endocrine syndrome. But there were no unified standards on the diagnosis， grade， TNM classification， and prognosis of GE-NETs. Early diagnosis rate of GE-NETs was low， and most of tumors were asymptomatic and detected at late stage， with a tendency to metastasize to the liver. Imaging examation was important in early diagnosis of GE-NETs. Surgery was the traditional first-line therapy and the only possible curative approach. Somatostatin analogues， such as long-term-release （LAR） octreotide， could relieve the hormonal symptoms， slow down the tumor growth， and had shown synergistic antiproliferative activity in combination with biological agent everolimus. Conclusions The lack of standardized classification and an incomplete understanding of this disease are some of the impediments to the progress of treatment. Individualized comprehensive therapy is the aim of the future treatment.
Objective
To review the research progress of the risk factors for slip progression and the pathogenesis of lumbosacral spondylolisthesis, and to discuss the value of Spinal Deformity Study Group (SDSG) classification system for lumbosacral spondylolisthesis.
Methods
Recent articles about the risk factors for slip progression and the pathogenesis of lumbosacral spondylolisthesis were reviewed and comprehensively analyzed with SDSG classification system of lumbosacral
spondylolisthesis.
Results
Pelvic incidence (PI) is the key pathogenic factor of lumbosacral spondylolisthesis. The Meyerding grade of slip, PI, sacro-pelvic balance, and spino-pelvic balance not only are the fundamental risk factors of slip progression, but also are the key factors to determine how to treat and influence the prognosis. Therefore, compared with Wiltse, Marchetti-Bartolozzi, and Mac-Thiong-Labelle classification systems of lumbosacral spondylolisthesis, SDSG classification based on these factors mentioned above, has better homogeneity between the subjects of subgroup, and better reliability, moreover, could better guide operative plan and judge the prognosis.
Conclusion
It is suggested that the SDSG classification system should be the standard classification for lumbosacral spondylolisthesis for the clinical and research work.
Objective
To investigate the self-made classification criterion for mucous cysts of distal interphalangeal joint and the effectiveness of the surgical treatment.
Methods
Between July 2008 and August 2015, 33 patients with mucous cysts of distal interphalangeal joint were treated, and the clinical data were retrospectively analyzed. Among 33 cases, 15 were male and 18 were female, aged from 45 to 74 years (mean, 58 years). The disease duration ranged from 3 weeks to 1 year (mean, 5 months). The cyst located at thumb in 18 cases, index finger in 4 cases, middle finger in 6 cases, ring finger in 4 cases, and little finger in 1 case. According to location, mucous cysts were typed: proximity to one side of the finger extensor tendon and deviation from the midline of the finger named lateral type (22 cases); and at both sides of the finger extensor tendon named intermediate type (11 cases). The bilateral digital dorsal incision was made in the intermediate type patients, and the reverse L-shaped incision was made in the lateral type patients, then the degenerative tissue was resected, the osteophyte was removed, and the capsule was repaired; finally, mucous cysts were resected.
Results
All the patients were followed up 4 months to 2 years (mean, 14.5 months). The incision healed by first intention without infection after operation. Clinical symptoms obtained improvement and no recurrence was observed in 29 patients except 4 patients who died of other diseases during follow-up.
Conclusion
A surgical treatment for mucous cysts of distal interphalangeal joint is feasible according to the self-made classification criterion and it has the advantage of simple operation and definite effectiveness.
Objective To recognize the different phases of Korotkoff sounds through deep learning technology, so as to improve the accuracy of blood pressure measurement in different populations. Methods A classification model of the Korotkoff sounds phases was designed, which fused attention mechanism (Attention), residual network (ResNet) and bidirectional long short-term memory (BiLSTM). First, a single Korotkoff sound signal was extracted from the whole Korotkoff sounds signals beat by beat, and each Korotkoff sound signal was converted into a Mel spectrogram. Then, the local feature extraction of Mel spectrogram was processed by using the Attention mechanism and ResNet network, and BiLSTM network was used to deal with the temporal relations between features, and full-connection layer network was applied in reducing the dimension of features. Finally, the classification was completed by SoftMax function. The dataset used in this study was collected from 44 volunteers (24 females, 20 males with an average age of 36 years), and the model performance was verified using 10-fold cross-validation. Results The classification accuracy of the established model for the 5 types of Korotkoff sounds phases was 93.4%, which was higher than that of other models. Conclusion This study proves that the deep learning method can accurately classify Korotkoff sounds phases, which lays a strong technical foundation for the subsequent design of automatic blood pressure measurement methods based on the classification of the Korotkoff sounds phases.
Post-traumatic stress disorder (PTSD) presents with complex and diverse clinical manifestations, making accurate and objective diagnosis challenging when relying solely on clinical assessments. Therefore, there is an urgent need to develop reliable and objective auxiliary diagnostic models to provide effective diagnosis for PTSD patients. Currently, the application of graph neural networks for representing PTSD is limited by the expressiveness of existing models, which does not yield optimal classification results. To address this, we proposed a multi-graph multi-kernel graph convolutional network (MK-GCN) model for classifying PTSD data. First, we constructed functional connectivity matrices at different scales for the same subjects using different atlases, followed by employing the k-nearest neighbors algorithm to build the graphs. Second, we introduced the MK-GCN methodology to enhance the feature extraction capability of brain structures at different scales for the same subjects. Finally, we classified the extracted features from multiple scales and utilized graph class activation mapping to identify the top 10 brain regions contributing to classification. Experimental results on seismic-induced PTSD data demonstrated that our model achieved an accuracy of 84.75%, a specificity of 84.02%, and an AUC of 85% in the classification task distinguishing between PTSD patients and non-affected subjects. The findings provide robust evidence for the auxiliary diagnosis of PTSD following earthquakes and hold promise for reliably identifying specific brain regions in other PTSD diagnostic contexts, offering valuable references for clinicians.
Objective To discuss the treatment of craniocerebral injuries caused by earthquake. Methods Retrospective analysis of clinical information for 256 patients with craniocerebral injury caused by an earthquake. Results The ‘Classification and Treatment’ was applied to the patients, whether or not they were operated on. A total of 146 patients were cured, 68 improved, 24 remained dependent on the care of others, and 8 died. The mortality rate was 3.13%. Conclusion Applying the ‘Classification and Treatment’ to patients with craniocerebral injury following an earthquake supported the use of medical resources and was associated with a low rate of death and disability.
This article aims to combine deep learning with image analysis technology and propose an effective classification method for distal radius fracture types. Firstly, an extended U-Net three-layer cascaded segmentation network was used to accurately segment the most important joint surface and non joint surface areas for identifying fractures. Then, the images of the joint surface area and non joint surface area separately were classified and trained to distinguish fractures. Finally, based on the classification results of the two images, the normal or ABC fracture classification results could be comprehensively determined. The accuracy rates of normal, A-type, B-type, and C-type fracture on the test set were 0.99, 0.92, 0.91, and 0.82, respectively. For orthopedic medical experts, the average recognition accuracy rates were 0.98, 0.90, 0.87, and 0.81, respectively. The proposed automatic recognition method is generally better than experts, and can be used for preliminary auxiliary diagnosis of distal radius fractures in scenarios without expert participation.
