Warranting the decision-maker,not the decision: How healthcare practitioners evaluate the legitimacy of patients’ unprompted requests for risk-reducing mastectomy

ObjectiveShared decision-making exists to reconcile healthcare practitioners’ responsibilities to respect patients’ autonomy whilst ensuring well-made decisions. Patients sometimes make unprompted requests for procedures that carry medical and other risks, such as risk-reducing mastectomy (RRM). Faced with pre-formed decisions into which they have had little input, it is unclear how practitioners can reconcile respecting autonomy with ensuring well-made decisions.MethodsQualitative study of linked patient-practitioner interviews in a breast unit in North-West England. We examined how 10 practitioners addressed 19 patients’ unprompted requests for RRM.ResultsPractitioners empathised with patients’ distress about cancer risk, regarded RRM as legitimate to help, but were wary of choices made ‘emotionally’. Practitioners did not seek to establish whether choices were well-made but, instead, ‘warranted’ patients by satisfying themselves that patients were ‘sensible’ and ‘informed’ decision-makers, and thus their decisions could be trusted. Practitioners provided information, and tested patients’ resolve by delaying decisions and presenting ‘what if’ scenarios depicting failure or harm from RRM.ConclusionPatients who present emotionally and with resolution can receive RRM without evidence of a well-made decision.Practice Implications: Argumentation theory proposes an ethically robust and clinically practicable approach, whereby practitioners elicit, examine and, where appropriate, challenge arguments underpinning patients’ decisions.     

Radiographic detection of porosities in removable partial denture castings

The purpose of this study was to discern the existence of structural defects in removable partial denture frameworks by means of radiographs. A simple technique was described using a dental radiograph unit and routine periapical dental films. All components of removable partial denture castings were radiographed with different angulations. The visibility of defects was clear. Structural defects were located and classified in two brands of removable partial denture base metal alloys (Ticonium and Nobilium) commonly used in fabrication of removable partial denture castings. The technique described is simple, inexpensive, not time-consuming, and can be implemented with minimum instrumentation. Future studies with this radiographic method for investigation of internal defects in fixed prostheses or dental implants are recommended.     

Chondrosarcoma of the mandible: a case report

Chondrosarcomas are malignant tumours of cartilaginous origin. They range from a well-differentiated growth resembling a benign cartilage tumour to a high-grade malignancy with aggressive local behaviour and the potential to metastasize. Only 5% to 10% of chondrosarcomas are known to occur in the head and neck region. A case of chondrosarcoma of the anterior region of the mandible is presented, along with a review of the relevant literature.     

Clinical,hematological, and imaging observations in a 25-year-old woman with abetalipoproteinemia

Abetalipoproteinemia is an uncommon cause of ataxia and retinitis pigmentosa (RP). Most of the neurological and ocular manifestations occur secondary to deficiency syndromes that is consequent to fat malabsorption from the small intestine. In this report, we have described the phenotype of a young adult female who manifested with recurrent diarrheal illness in her first decade, followed by anemia, RP, and neurological involvement with progressive deafness, cerebellar and sensory ataxia, and subclinical neuropathy in her second decade of life. While RP and sensory ataxia due to vitamin E deficiency are well-recognized features of abetalipoproteinemia, deafness is rarely described. In addition, we have highlighted the abnormal posterior column signal changes in the cervical cord in this patient. Early recognition avoids unnecessary investigations and has a potential to retard the disease progression by replacing some of the deficient vitamins.     

Epilepsy classification using optimized artificial neural network

ABSTRACT

Objectives: An Electroencephalogram (EEG) is the result of co-operative actions performed by brain cells. In other words, it can be defined as the time course of extracellular field potentials that are generated due to the synchronous action of cells. It is widely used for the analysis and diagnosis of several conditions. But this clinical data use to be multi-dimensional, context-dependent, complex, and it causes a concoction of various computing related research challenges. The objective of this study was to develop a computer-aided diagnosis system for epilepsy detection using EEG signals to ease the diagnosis process.

Materials: In this study, EEG datasets for epilepsy disease detection were taken from a public domain (Bonn University, Germany). These EEG recordings contain 100 single-channel EEG signals with maximum duration of 23.6 seconds. This data set was recorded intra-cranially and extra-cranially with the help of a 128-channel amplifier system using a common reference point.

Results: For a unique set of EEG signal features, the Optimized Artificial Neural Network model for classification and validation was developed with optimum neurons in the hidden layer. Results were tested on the basis of accuracy, sensitivity, precision, and specificity for all classes. The proposed Particle Swarm Optimized Artificial Neural Network provided 99.3% accuracy for EEG signal classification.

Discussion: Our results indicate that artificial neural network has efficiency to provide higher accuracy for epilepsy detection if the statistical features are extracted carefully. It is also possible to improve results for real time diagnosis by using optimization technique for error reduction.

Abbreviations: EEG: Electroencephalogram CAD: Computer-Aided Diagnosis ANN: Artificial Neural Network PSO: Particle Swarm Optimization FIR: Finite Impulse Response IIR: Infinite Impulse Response MSE: Mean Square Error.