7 research outputs found

    Ranking of Fuzzy Similar Faces Using Relevance Matrix and Aggregation Operators

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    AbstractIn perception based imaging, Sketching With Words (SWW) is a well-established methodology in which the objects of computation are fuzzy geometric objects (f-objects).The problem of facial imaging of criminal on the basis of onlooker statement is not lack of method and measures but the modeling of onlooker(s) mind set. Because the onlooker has to give statements about different human face parts like forehead, eyes, nose, and chin etc.The concept of fuzzy similarity (f-similarity) and proper aggregation of components of face may provide more flexibility to onlooker(s). In proposed work onlooker(s) statement is recorded. Thereafter it is compared with existing statements. The f-similarity with different faces in database is estimated by using β€˜as many as possible’ linguistic quantifier. Three types of constraints over size of parts of face β€˜small’, β€˜medium’, and β€˜large’ are considered. Possibilistic constraints with linguistic hedges and negation operator like β€˜very long’, β€˜not long’, β€˜not very long’ etc. are used. Moreover we have generated ranking of alike faces in decreasing order by using the concepts of f-similarity and relevance matrix

    Illness cognitions in head and neck squamous cell carcinoma: predicting quality of life outcome

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    Goals of work: This paper presents an observational study of the longitudinal effects of cancer treatment on quality of life (QoL) in patients treated for head and neck squamous cell carcinoma (HNSCC), and evaluated the contribution of patients' baseline illness cognitions to the prediction of QoL 2 years after diagnosis. Patients and methods: One hundred seventy-seven patients eligible for primary treatment for HNSCC completed the Illness Perception Questionnaire-Revised at baseline and the European Organization for Research and Treatment of Cancer Core Quality of Life Questionnaire-30 at baseline, at 1-year and 2-year follow-ups. Main results Compared to baseline, patients reported better emotional functioning at both follow-ups (p<0.001), worse social functioning at 12 months (p<0.05), and better global health

    Predictive factors for pharyngocutaneous fistulization after total laryngectomy: a Dutch Head and Neck Society audit

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    Incidences of pharyngocutaneous fistulization (PCF) after total laryngectomy (TL) reported in the literature vary widely, ranging from 2.6 to 65.5%. Comparison between different centers might identify risk factors, but also might enable improvements in quality of care. To enable this on a national level, an audit in the 8 principle Dutch Head and Neck Centers (DHNC) was initiated. A retrospective chart review of all 324 patients undergoing laryngectomy in a 2-year (2012 and 2013) period was performed. Overall PCF%, PCF% per center and factors predictive for PCF were identified. Furthermore, a prognostic model predicting the PCF% per center was developed. To provide additional data, a survey among the head and neck surgeons of the participating centers was carried out. Overall PCF% was 25.9. The multivariable prediction model revealed that previous treatment with (chemo)radiotherapy in combination with a long interval between primary treatment and TL, previous tracheotomy, near total pharyngectomy, neck dissection, and BMI  < 18 were the best predictors for PCF. Early oral intake did not influence PCF rate. PCF% varied quite widely between centers, but for a large extend this could be explained with the prediction model. PCF performance rate (difference between the PCF% and the predicted PCF%) per DHNC, though, shows that not all differences are explained by factors established in the prediction model. However, these factors explain enough of the differences that, compensating for these factors, hospital is no longer independently predictive for PCF. This nationwide audit has provided valid comparative PCF data confirming the known risk factors from the literature which are important for counseling on PCF risks. Data show that variations in PCF% in the DHNCs (in part) are explainable by the variations in these predictive factors. Since elective neck dissection is a major risk factor for PCF, it only should be performed on well funded indicatio

    Variation in Integrated Head and Neck Cancer Care: Impact of Patient and Hospital Characteristics

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    Background: Monitoring and effectively improving oncologic integrated care requires dashboard information based on quality registrations. The dashboard includes evidence-based quality indicators (QIs) that measure quality of care. This study aimed to assess the quality of current integrated head and neck cancer care with QIs, the variation between Dutch hospitals, and the influence of patient and hospital characteristics. Methods: Previously, 39 QIs were developed with input from medical specialists, allied health professionals, and patients’ perspectives. QI scores were calculated with data from 1,667 curatively treated patients in 8 hospitals. QIs with a sample size of >400 patients were included to calculate reliable QI scores. We used multilevel analysis to explain the variation. Results: Current care varied from 29% for the QI about a case manager being present to discuss the treatment plan to 100% for the QI about the availability of a treatment plan. Variation between hospitals was small for the QI about patients discussed in multidisciplinary team meetings (adherence: 95%, range 88%–98%), but large for the QI about malnutrition screening (adherence: 50%, range 2%–100%). Higher QI scores were associated with lower performance status, advanced tumor stage, and tumor in the oral cavity or oropharynx at the patient level, and with more curatively treated patients (volume) at hospital level. Conclusions: Although the quality registration was only recently launched, it already visualizes hospital variation in current care. Four determinants were found to be influential: tumor stage, performance status, tumor site, and volume. More data are needed to assure stable results for use in quality improvement
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