Evaluation of appendicitis risk prediction models in adults with suspected appendicitis

Background Appendicitis is the most common general surgical emergency worldwide, but its diagnosis remains challenging. The aim of this study was to determine whether existing risk prediction models can reliably identify patients presenting to hospital in the UK with acute right iliac fossa (RIF) pain who are at low risk of appendicitis. Methods A systematic search was completed to identify all existing appendicitis risk prediction models. Models were validated using UK data from an international prospective cohort study that captured consecutive patients aged 16–45 years presenting to hospital with acute RIF in March to June 2017. The main outcome was best achievable model specificity (proportion of patients who did not have appendicitis correctly classified as low risk) whilst maintaining a failure rate below 5 per cent (proportion of patients identified as low risk who actually had appendicitis). Results Some 5345 patients across 154 UK hospitals were identified, of which two‐thirds (3613 of 5345, 67·6 per cent) were women. Women were more than twice as likely to undergo surgery with removal of a histologically normal appendix (272 of 964, 28·2 per cent) than men (120 of 993, 12·1 per cent) (relative risk 2·33, 95 per cent c.i. 1·92 to 2·84; P < 0·001). Of 15 validated risk prediction models, the Adult Appendicitis Score performed best (cut‐off score 8 or less, specificity 63·1 per cent, failure rate 3·7 per cent). The Appendicitis Inflammatory Response Score performed best for men (cut‐off score 2 or less, specificity 24·7 per cent, failure rate 2·4 per cent). Conclusion Women in the UK had a disproportionate risk of admission without surgical intervention and had high rates of normal appendicectomy. Risk prediction models to support shared decision‐making by identifying adults in the UK at low risk of appendicitis were identified

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Slow Learner Prediction Using Multi-Variate Naïve Bayes Classification Algorithm

Machine Learning is a field of computer science that learns from data by studying algorithms and their constructions. In machine learning, for specific inputs, algorithms help to make predictions. Classification is a supervised learning approach, which maps a data item into predefined classes. For predicting slow learners in an institute, a modified Naïve Bayes algorithm implemented. The implementation is carried sing Python.  It takes into account a combination of likewise multi-valued attributes. A dataset of the 60 students of BE (Information Technology) Third Semester for the subject of Digital Electronics of University Institute of Engineering and Technology (UIET), Panjab University (PU), Chandigarh, India is taken to carry out the simulations. The analysis is done by choosing most significant forty-eight attributes. The experimental results have shown that the modified Naïve Bayes model has outperformed the Naïve Bayes Classifier in accuracy but requires significant improvement in the terms of elapsed time. By using Modified Naïve Bayes approach, the accuracy is found out to be 71.66% whereas it is calculated 66.66% using existing Naïve Bayes model. Further, a comparison is drawn by using WEKA tool. Here, an accuracy of Naïve Bayes is obtained as 58.33 %

Slow Learner Prediction Using Multi-Variate Naïve Bayes Classification Algorithm

Machine Learning is a field of computer science that learns from data by studying algorithms and their constructions. In machine learning, for specific inputs, algorithms help to make predictions. Classification is a supervised learning approach, which maps a data item into predefined classes. For predicting slow learners in an institute, a modified Naïve Bayes algorithm implemented. The implementation is carried sing Python.  It takes into account a combination of likewise multi-valued attributes. A dataset of the 60 students of BE (Information Technology) Third Semester for the subject of Digital Electronics of University Institute of Engineering and Technology (UIET), Panjab University (PU), Chandigarh, India is taken to carry out the simulations. The analysis is done by choosing most significant forty-eight attributes. The experimental results have shown that the modified Naïve Bayes model has outperformed the Naïve Bayes Classifier in accuracy but requires significant improvement in the terms of elapsed time. By using Modified Naïve Bayes approach, the accuracy is found out to be 71.66% whereas it is calculated 66.66% using existing Naïve Bayes model. Further, a comparison is drawn by using WEKA tool. Here, an accuracy of Naïve Bayes is obtained as 58.33 %

Comparison of incidence of dentinal defects after root canal preparation with continuous rotation and reciprocating instrumentation

AbstractBiomechanical preparation is one of the most important steps in endodontic therapy. Rotary instrumentation has facilitated this step. Nowadays the market is flooded with different types of rotary instruments. The present study compared the root dentinal crack formation with continuous rotating versus reciprocating root canal preparation methods. One hundred and fifty freshly extracted teeth were used for the study. They were divided into 5 groups with 30 teeth in each group. Thirty teeth were kept under control group A and no root canal preparation was done for this group. Another 30 teeth were prepared with hand files which were kept under control group B. In the experimental groups (sample size, n=30 each) root canals were prepared with ProTaper, K3XF rotary system and WaveOne. Sectioning of these teeth was done at 3, 6 and 9mm from the apex and were evaluated for the presence of any defects. Root dentinal cracks were produced with each type of rotary instruments. Statistical analysis showed no significant difference in root dentinal crack formation between control groups and WaveOne system. There was statistically significant difference in root dentinal crack formation when the canals were prepared with ProTaper and K3XF rotary system. So it was concluded, that continuous rotating instruments could produce dentinal crack formation. Root canal instruments with reciprocating movement appear to be a better option than continuous rotation movement

Evaluation of thyroid nodules classified as Bethesda category III on FNAC

Background: The Bethesda (BSRTC) category III has been ascribed a malignancy rate of 5-15%, however, the probability of malignancy remains variable. Aim: To evaluate category III with respect to its rate and risk of malignancy and substratify it. Settings and Design: Atypia of undetermined significance/Follicular lesion of undetermined significance (AUS/FLUS) percentage, cytohistological correlation, and risk of malignancy were analyzed and substratification was done. Material and Methods: Category III cases over a 2-year period were analyzed retrospectively. Statistical Analysis: Two-tailed Fisher exact test, with a level of significance set at 0.05, was performed for data analysis. Results: Of 1169 thyroid fine needle aspirations (FNAs), 76 (6.5%) were category III. A total of 48 patients had follow up; 24 patients underwent surgery, 12 repeat FNA, and 12 were clinically followed. Repeat FNA cytology was unsatisfactory in 8.3%, benign in 66.7%, AUS in 8.3%, and follicular neoplasm in 16.7%. Of the 24 operated, 8 (33.3%) were malignant (follicular variants of papillary thyroid carcinoma), 5 (20.8%) were follicular adenomas, and 11 (45.8%) were non-neoplastic. Among all AUS/FLUS nodules with follow-up, malignancy was confirmed in 16.7% (8/48) whereas with nodules triaged to surgery only, the malignancy rate was 33.3% (8/24). Substratification into categories of "cannot exclude PTC" and "favor benign" helped detect malignancy better, as 85.7% cases in the first subcategory (P < 0.001) and none (P < 0.02) in the last proved malignant. Conclusion: Though the rate of Category III in our study is in accordance to BSRTC, the risk of malignancy in AUS/FLUS nodules is higher. Substratification of AUS/FLUS may help better patient management

Occult gallbladder carcinoma presenting as a primary ovarian tumor in two women: two case reports and a review of the literature

<p>Abstract</p> <p>Introduction</p> <p>The ovary is a common site of metastasis from various organs. However, little is known about gallbladder carcinoma metastasizing to the ovaries and presenting as a primary ovarian tumor.</p> <p>Case presentation</p> <p>We report two cases of a metastatic gallbladder carcinoma which mimicked a primary ovarian tumor in a 35-year-old and a 62-year-old North Indian woman. Clinically, both our patients presented with abdominal masses without obvious signs and symptoms related to gallbladder carcinoma. Radiology suggested the possibility of a primary ovarian tumor with chronic cholecystitis and cholelithiasis. The gross features also mimicked a primary malignant ovarian tumor in the first case and a benign mucinous neoplasm in the second case. Exact diagnoses could only be made after thorough sampling from both the ovaries and gallbladder.</p> <p>Conclusions</p> <p>Gallbladder carcinoma with metastasis to the ovaries can mimic both malignant and benign primary ovarian tumors. Extensive cystic change in the ovary due to metastasis from gallbladder carcinoma has rarely been reported. A high index of suspicion and thorough sampling are essential to avoid misdiagnosis in such cases.</p