Percentage Of False Negatives Research Articles

Assessment of the Role of a Second Evaluation of Capsule Endoscopy Recordings to Improve Diagnostic Yield and Patient Management

Introduction: The diagnostic yield (DY) of small-bowel capsule endoscopy (SBCE) varies considerably according to its indication. Some strategies have been used to increase DY with varying results. The intention of this study was to identify whether evaluation of the SBCE recordings by a second reviewer can increase DY and change patient management. Methods: One hundred SBCEs with different indications, already read by an endoscopist were read by a second blinded endoscopist. When the results of the 2 readings were different, the images were discussed by the endoscopists; if no consensus was reached, they took the opinion of a third endoscopist into account. All the participating endoscopists had experience in reading SBCEs (i.e., >50 per year). The SBCE findings were divided into positive (vascular lesions, ulcers, and tumors), equivocal (erosions or red spots), and negative. The interobserver agreement and the increase in DY were assessed as well as the percentage of false-negatives (FNs) in the first evaluation. Results: The indications for SBCE were small-bowel bleeding (SBB) in 48 cases, Crohn’s disease (CD) in 30, and other causes (iron-deficiency anemia, small-bowel tumors, and diarrhea) in 22. There was substantial interobserver agreement between evaluations (κ = 0.79). The findings in the first evaluation were positive in 60%, equivocal in 20% and, negative in 20%. In the second evaluation, 66% were positive, 18% were equivocal, and 16% were negative. The increase in DY with the second reading was 6% (p = 0.380), i.e., 6.3% for SBB, 4.4% for CD and 9.2% for other indications, resulting in a change in management of 4% of the patients. FNs in the first SBCE reading were found in 4% of the SBCEs. Discussion: A second evaluation of the SBCE recordings identified significant pathology that the first evaluation had missed, resulting in a nonsignificant 6% increase in DY and a change in the management of 4% of the patients.

Validation of a novel diagnostic test for assessing the risk of urinary uric acid crystallization

Background and aimsUric acid (UA) kidney stones account for 10 to 11% of all kidney stones, and this percentage has increased over time. An accurate, rapid, simple, and low-cost test is needed to distinguish urine that is susceptible and resistant to the formation of UA crystals. The aim of this paper is to develop a test to assess the risk for UA crystallization (RUAC) and to validate its utility in routine clinical practice by analysis of urine samples of UA stone formers and healthy volunteers. Patients and MethodsUrine samples of 20 healthy adult volunteers and 54 active formers of UA stones were collected. Three samples were collected from each participant, with at least 7 days between each collection. The main lithogenic parameters for UA stones were determined, and an RUAC test was performed in all urine samples. ResultsOur RUAC test reliably discriminated urine that was resistant and susceptible to the formation of UA crystals. This test had high specificity (94%) and a low percentage of false negatives. ConclusionThe RUAC test described here had high diagnostic accuracy, low-cost, and a rapid assay time, that make this test an attractive screening tool for UA stone fomers follow-up.

Heating Up Cold Cases: An Interview with Bruce Budowle on Human Identification

Heating Up Cold Cases: An Interview with Bruce Budowle on Human Identification

COVID concerns highlight the importance of adequate fecal immunochemical test sample collection.

COVID concerns highlight the importance of adequate fecal immunochemical test sample collection.

Background subtraction and yolo algorithm

Introduction: This article is the result of research entitled “Signal processing system for the detection of people in agglomerations in areas of public space in the city of Cúcuta”, developed at the Universidad Francisco de Paula Santander in 2020.Problem: The high percentage of false positives and false negatives in people detection processes makes decision making in video surveillance, tracking and tracing applications complex. Objective: To determine which technique for the detection of people presents better results in terms of respon-se time and detection hits.Methodology: Two techniques for the detection of people in uncontrolled environments are validated in Python with videos taken inside the Universidad Francisco de Paula Santander: Background subtraction and the YOLO algorithm.Results: With the background subtraction technique, we obtained a hit rate of 84.07 % and an average response time of 0.815 seconds. Likewise, with the YOLO algorithm the hit rate and average response time are 90% and 4.59 seconds respectively.Conclusion: It is possible to infer the use of the background subtraction technique in hardware tools such as the Pi 3B+ Raspberry board for processes in which the analysis of information in real time is prioritized, while the YOLO algorithm presents the characteristics required in the processes in which the information is analyzed after the acquisition of the image.Originality: Through this research, aspects required for the real-time analysis of information obtained in pro-cesses of people detection in uncontrolled environments were analyzed. Limitations: The analyzed videos were taken only at the Universidad Francisco de Paula Santander. Also, the Raspberry Pi 3B+ board overheats when processing the video images, due to the full resource requirement of the device.

Visualized Prostate Biopsy: An Intuitive Three-Dimensional User Interface for Systematic and Targeted Biopsy.

Background: Prostate biopsy false negative percentages are 21% to 47% and 16% to 30% for systematic and fused biopsy, respectively. An intuitive three-dimensional (3D) observed user interface may help reduce these percentages by providing real-time guidance and feedback during transrectal or transperineal biopsy. Materials and Methods: We track the moving prostate (including template locations and regions of interest), the transrectal ultrasound (TRUS) probe, and the biopsy device to construct a 3D environment. Users observe, aim, sample, and receive feedback in real time. Using a simulator with simulated TRUS, 48 participants performed freehand systematic prostate biopsy with traditional TRUS guidance and afterward with visualized prostate biopsy (vPBx). Results: During simulated biopsy, vPBx reduced the false negative percentage for a 0.5-mL spherical apical lesion from 52% to 2% (p < 0.001). Conclusions: Preliminary results during simulated systematic biopsy warrant retrofitting the vPBx to actual TRUS equipment as a step toward clinical trials with patients.

Evaluation Of Breast Lesions By Fine Needle Aspiration Cytology And Correlation With Histopathological Examination

INTRODUCTION: Breast cancer is the most common cancer in worldwide between age of 20-59 years females. It is responsible for 15% of total deaths due to cancers. FNAC is one of the important components of ‘triple approach’, which has been widely accepted for the preoperative diagnosis of breast lesions. AIM: To establish utility and effectiveness in diagnosis of breast lesion by FNAC and find the incidence of breast cancer in palpable lump in different age groups. MATERIAL AND METHODS: In this study100 cases of breast lump referred by the Surgery Department of LG Hospital was included in study period fromAugust2016toOctober2018. RESULT: Out of the 100 cases, in 99 cases the aspirates were adequate for interpretation. Out of 100, 90cases underwent a biopsy and could be correlated histopathologically. According to NHSBSP, there was 1 case in C1 category,64 cases in C2 category, 3 cases in C3 category, 2 cases in C4 category and 30casesin C5 category. CONCLUSION: The present study categorized by NHSBSP criteria conclude 100% confirmation in C4 and C5 category in age group above 40 years showing malignancy by histopathology. The overall sensitivity in the study was 83.33%, specificity was 100%, positive predictive value in disease was 100% and the false negative percentage was 16.66%. FNAC is an accurate and reliable preoperative diagnostic tool for breast lesions.

A Liquidity Risk Early Warning Indicator for Italian Banks: A Machine Learning Approach

The paper develops an early warning system to identify banks that could face liquidity crises. To obtain a robust system for measuring banks’ liquidity vulnerabilities, we compare the predictive performance of three models – logistic LASSO, random forest and Extreme Gradient Boosting – and of their combination. Using a comprehensive dataset of liquidity crisis events between December 2014 and January 2020, our early warning models’ signals are calibrated according to the policymaker's preferences between type I and II errors. Unlike most of the literature, which focuses on default risk and typically proposes a forecast horizon ranging from 4 to 6 quarters, we analyse liquidity risk and we consider a 3-month forecast horizon. The key finding is that combining different estimation procedures improves model performance and yields accurate out-of-sample predictions. The results show that the combined models achieve an extremely low percentage of false negatives, lower than the values usually reported in the literature, while at the same time limiting the number of false positives.

Targeted education as a method for reinforcing Paris System criteria and reducing urine cytology atypia rates

The Paris System for Urine Cytology (TPS) provides well-defined diagnostic criteria for the category of atypical urothelial cells (AUC). The current study compares the rate of AUC diagnoses at a large academic medical center before and after an educational intervention (EI) by a urine cytology expert. An expert in TPS delivered an educational intervention consisting of an interactive microscope session and a didactic session that focused on the AUC diagnostic category. The number of urine cytology cases, the AUC rate, and the false-negative percentage were calculated before and after the EI, using the electronic medical records and cytologic-histologic correlation records. A total of 4026 urine cytology cases were signed out in the 25 months prior to the educational intervention and 1585 cases were signed out in the 10 months after the intervention. EI had a significant impact on diagnostic categorization, including a reduction in AUC (19.6% versus 12.5%) and suspicious for high-grade urothelial carcinoma (3.9% versus 3.1%) diagnoses. The cytotechnologists also placed fewer cases into the AUC category during primary screening (27.6% versus 23.0%). Although a higher percentage of cases was reported as negative for high-grade urothelial carcinoma, the false-negative rate did not significantly change after the intervention (1.8% versus 2.0% of negative cases, P = 0.65). Focused educational sessions for pathologists and cytotechnologists on the diagnostic criteria for AUC as defined by TPS can significantly reduce the rate of atypical diagnoses without a significant increase in the rate of false negatives.

Towards a Hybrid Immune Algorithm Based on Danger Theory for Database Security

In Databases, the most prevalent cause of data breaches comes from insiders who misuse their account privileges. Due to the difficulty of discovering such breaches, an adaptive, accurate, and proactive database security strategy is required. Intrusion detection systems are utilized to detect, as fast as possible, user's account privilege misuse when a prevention mechanism has failed to address such breaches. In order to address the foremost deficiencies of intrusion detection systems, artificial immune systems are used to tackle these defects. The dynamic and more complex nature of cybersecurity, as well as the high false positive rate and high false negative percentage in current intrusion detection systems, are examples of such deficiency. In this paper, we propose an adaptable efficient database intrusion detection algorithm based on a combination of the Danger Theory model and the Negative Selection algorithm from artificial immune system mechanisms. Experimental results for the implementation of the proposed algorithm provide a self-learning mechanism for achieving high detection coverage with a low false positive rate by using the signature of previously detected intrusions as detectors for the future detection process. The proposed algorithm can enhance detecting insider threats and eliminate data breaches by protecting confidentiality, ensuring integrity, and maintaining availability. To give an integrated picture, a comprehensive and informative survey for the different research directions that are related to the proposed algorithm is performed.

Screening for Cervical Cancer by Sequential Examination of the Cervix

Background: cervical cancer carries a death sentence in the mind of every patient. Since it has a long history; it can be cured if caught early. Pap smear screening has been effective in reducing rates of cervical cancer, still, it has some draws back especially low sensitivity. The study aimed to increase the detection of Pap- tests by sequential examination of the cervix by Pap smear and colposcopy and to demonstrate the demographic criteria of those affected.&#x0D; Material and method: A Comparative clinical study, conducted in the department of Obstetrics and Gynecology in AL-Yarmouk hospital /Baghdad, Iraq from 2017-2018. One hundred sexually active female fits into our inclusion and exclusion criteria were invited to participate after we explain it is aim, all participant filled a questioner sheet and were screened by Pap-smear then immediately by colposcopy exam.&#x0D; Result: history of post-coital bleeding and bleeding on touch scored meaningful P-value&lt;0.05, Pap-smear examination shows 20% as abnormal study stratified into 13/20 CIN I [Cervical Intraepithelial Neoplasm], 3/20 CIN II, 2/20 CIN III, and 2/20 malignant cases. The colposcopy exam showed 32/100 abnormal cases necessitating directed histological biopsies. Examination confirms 20/32 CIN I, 7/32 CIN II, 2/32 CIN III, and 3/32 cases with cervical malignancy.&#x0D; Conclusions: cervical smear had specificity and positive predictive value of 100% yet it missed 11 cases diagnosed with colposcopy one was malignant. As a result, Pap-smear alone can detect up to 18% of the pre-cancerous lesion of the cervix, this figure increases to 29% when dumbing the results of the colposcopic directed biopsy. The sensitivity of the test was 62.5% with a false-negative percentage of 37.5%, having a total 88% accuracy rate and a negative predictive value of 85%, this study confirms the benefits of the combined method of screening and may be used to increase pap-smear sensitivity.

Multiplex real-time PCR for detection and quantification of Colletotrichum abscissum and C. gloeosporioides on Citrus leaves

Colletotrichum abscissum and C. gloeosporioides are the causal agents of citrus Postbloom Fruit Drop (PFD), a major disease in several countries of the American continent. These pathogens infect only floral structures; however, in the absence of floral tissue, they survive asymptomatically on the leaf tissue. Thus, detecting and quantifying pathogens on leaves are critical for epidemiological studies. Colletotrichum species are detected through isolation, serological methods and conventional PCR, but these techniques are not effective for accurate detection of PFD agents. This study aimed to develop specific, more sensitive, faster and less laborious techniques than the traditional ones routinely used. We developed, standardized and validated a multiplex real-time PCR with high sensitivity (98–99.3%) and specificity (94.3–97.1%) for the detection and quantification of both pathogens. This technique was 1000 and 10,000 times more sensitive than Nested-PCR and PCR, respectively for the detection of C. abscissum. Similarly, the multiplex qPCR was 100 and 1000 times more sensitive than Nested-PCR and PCR, respectively for the detection of C. gloeosporioides. The diagnostic parameters used to validate the multiplex qPCR showed a high positive (17–35) and low negative (0.006–0.95) likelihood ratios, low percentage of false positives (2.8–5.6%) and false negatives (0.6–1.9%), good agreement between the results generated between qPCR and multiplex qPCR, revealed by the Kappa index (0.91–0.98) and coincidental results (95–99%). All parameters indicated that the multiplex qPCR was effectively validated to detect and quantify the causal agents of PFD.

Random forest-based robust classification for lithographic hotspot detection

With continuous downscaling of feature sizes, potentially problematic patterns (hotspots) have become a major issue in generation of optimized mask design for better printability. The lithography process sensitive patterns in a design lead to degradation of both electrical performance and manufacturing yield of the integrated circuit. Due to sequential flow of very large-scale integration (VLSI) design and manufacturing, missing any hotspot has an adverse impact on product turnaround time and cost. The lithographic samples are generally defined using a combination of continuous variables (to represent aerial image and pattern density) and categorical variables (to represent allowed layout design rules). The conventional hotspot classification techniques suffer from suboptimum performance due to their inability to efficiently represent and use the above-mentioned feature metrics. In general, the number of hotspots in the lithographic data is much less compared to the total number of patterns in a full-chip design. It makes the input data imbalanced and adds additional difficulties in the decision making processes. We present a robust technique to detect the process sensitive patterns using random forest-based machine learning technique. The emphasis is put on the layout features extraction techniques to improve the performance of the proposed approach. The simulation results show that the patterns susceptible to variations under different dose and focus conditions undergo a drastic change in their aerial image characteristics even when the geometry is varied by a very small margin. We observed from our analysis that the minimum number of false negatives can be achieved with reasonable increase in the number false positives. Moreover, compared to conventional hotspot classification techniques, we are able to achieve a very low percentage of false negatives with a binary classifier trained on an imbalanced dataset. Another key observation from our analysis is that the random forest method can obtain the most representative heuristics required to define categories from the lithographic datasets with continuous and categorical variables. In addition, our proposed approach can easily be integrated with commercially available electronic design automation tools and in-house design simulators to make the process flow viable in terms of a business perspective.

Active Matrix Metalloproteinase-8 Point-of-Care (PoC)/Chairside Mouthrinse Test vs. Bleeding on Probing in Diagnosing Subclinical Periodontitis in Adolescents.

This cross-sectional study compares the effectiveness of an active MMP-8 (aMMP-8) point-of-care (PoC)/chairside mouthrinse test to the conventional bleeding on probing (BOP) (cutoff 20%) test in detecting subclinical periodontitis/pre-periodontitis in Finnish adolescents. The study was carried out at the Kotka Health Center, Finland. A total of 47 adolescents (30 boys/17 girls) aged 15–17 were first tested with the aMMP-8 PoC test, followed by a full-mouth evaluation of clinical parameters of oral health including periodontal, oral mucosal, and caries assessment. A periodontist performed these clinical examinations. The aMMP-8 PoC test result had much stronger association with subclinical periodontitis than the BOP 20% test (2.8–5.3 times stronger in terms of odds ratio). The aMMP-8 PoC test had ≥2 times higher sensitivity than the BOP 20% test with, generally, the same specificity. Further, the aMMP-8 PoC test had generally better accuracy and lower false negative percentages. The aMMP-8 PoC test seemed to be more effective than the conventional BOP test in detecting subclinical periodontitis/pre-periodontitis in adolescents reducing the risk of their undertreatment. However, the sample size may be a limiting factor, and more studies are needed to confirm our results for both adolescents and adults.

SEC‐H5: Secure and efficient integration of settings of enhanced HTML5 XSS vector defensive framework on edge network of fog nodes

SummaryThe authors introduce an enhanced HTML5 (H5) Cross‐Site Scripting (H5‐XSS) attack vector defensive model that safeguards and maintains the confidentiality of users accessing the web applications hosted on the fog nodes. Initially, the model performs in self‐training manner and estimates some features of H5 script code at the simulated desktop host systems of cloud infrastructure. Second, a process of H5 feature estimation gets re‐executed for the online generated HTTP response on the edge network of fog device. Any oddity in these two features sets results in the consequent nested context‐familiar sanitization of the distrustful script code on an installed fog nodes. The author design their framework structure on an open application infrastructural structure of Cisco IOx framework of various networked edge devices as fog nodes. The authors configure the tested bed of H5 web platforms on the nodes of the fog computing network for evaluating the malicious script alleviation capability of their model. Evaluation outcomes expose that the author's technique is skilled sufficient for sensing and excluding the suspicious H5 code with sufferable percentage of False Negatives (FNs), False Positives (FPs), and reduced overall performance overhead during the peak congestion of generation of sanitized HTTP response on the fog nodes.

Statistical Comparison of Afirma Gsc and Afirma Gec Outcomes in a Community Endocrine Surgical Practice: Early Findings

The Veracyte Afirma Gene Expression Classifier (GEC) has been the most widely used negative predictive value molecular classifier for indeterminate cytology thyroid nodules since January 2011. To improve the specificity and further reduce unnecessary thyroid surgeries, a second-generation assay (Afirma Genetic Sequence Classifier [GSC]) was released for clinical use in August 2017. We report 11 months of clinical outcomes experience with the GSC and compare them to our 6.5-year experience with the GEC. We searched our practice registry for FNAB nodules with Afirma results from January 2011through June 2018. GEC versus GSC results were compared overall, in oncocytic and nononcocytic aspirates and by pathologic outcomes. GSC identified less indeterminate cytology nodules as suspicious (38.8%; 54/139) when compared to GEC (58.4%; 281/481). There was a decrease of in the percentage of oncocytic fine-needle aspiration thyroid biopsy (FNAB) subjects classified as suspicious in the GSC group, with 86 of 104 oncocytic indeterminates (82.7%) classified as suspicious by GEC and 12 of 34 (35.3%) classified as suspicious by GSC. The surgery rate in patients with oncocytic aspirates fell from 56% in the GEC group to 31% in the GSC-evaluated group (45%). Pathology analysis demonstrated a false-negative percentage for an incomplete surgical group of 9.5% for GEC and 1.2% for GSC. Our GSC data suggest that the GSC further reduces surgery in indeterminate thyroid nodules by improving the specificity of Afirma technology without compromising sensitivity. A primary determinant for this change is a significant improvement in the specificity of the Afirma GSC test in oncocytic FNAB aspirates. FNAB = fine-needle aspiration biopsy; GEC = Gene Expression Classifier; GSC = Genetic Sequence Classifier.

PRM75 - DEVELOPMENT OF MACHINE LEARNING BASED ABSTRACT DOCUMENT CLASSIFICATION FOR SUPPORTING SYSTEMATIC REVIEWS

Machine learning is a predictive computational method which enables rapid automatic data classification, and thus could replace manual and time-consuming work. Prior to its implementation, however, careful evaluation of the prediction accuracy is necessary. This study evaluated the possibility of using a machine learning method for abstract document classification of research articles in the process of systematic review. We used Long Short-Term Memory networks (LSTM), a type of recurrent neural network architecture, in order to understand the contexts of the abstracts. Owing to the limited number of articles in one dataset, we pre-trained the LSTM by feeding the model sequences of words, and subsequently performed virtual adversarial training to stabilize LSTM learning. We used two distinct datasets. The first dataset contained 1307 articles which had previously been selected manually. Five exclusion criteria were applied to metadata about each article, and on this basis were either qualified for use (categorized into a “use” group) or excluded (categorized into “non-use” group). From each group, 60%, 20%, and 20% of randomly selected data were used for training, validation, and testing of LSTM, respectively. In the test phase, 97% of the data was correctly classified into the “non-use” group (3% false positives), whereas only 73% of data was correctly classified into the “use” group (27% false negatives). The second dataset contained 1023 articles. In the test phase, the percentage of false positives and negatives was 5% and 23%, respectively. Our results indicate that although the false positive classification was sufficiently low, the false negative classification was not negligible. Reducing false negatives is necessary for the implementation of this approach as false negatives are more critical for supporting systematic reviews than false positives.

113 Ultrasound of the Optic Nerve Sheath as an Indicator of Mild Traumatic Brain Injury: Animal Model Provides Anatomical Basis

With no definitive method for diagnosis, many cases of mild traumatic brain injury (mTBI) receive suboptimal clinical management. Since emergency physicians and emergency medical services personnel are on the front lines with regard to mTBI, a tool for more accurate and timely indication of mTBI could improve diagnosis and management of mTBI in acute settings. Clinically, we have begun to validate that ultrasound measurements of optic nerve sheath dilation can be used to indicate mTBI. The objective of this study was to investigate the anatomical mechanism behind what has been observed clinically. More specifically, we evaluated the effect of increased intracranial pressure (ICP) on the fiber bundles connecting the optic nerve to the optic sheath in a swine model. Four experimental, 2 sham, and 4 normal swine were used in this study. In the experimental swine models, increased ICP was induced by passing a Foley catheter through a drilled opening into the epidural space and inflating a balloon. ICP was monitored continuously. For all animals, a section of optic nerve sheath with the intact nerve was removed after euthanasia and enucleation. The tissue was fixed overnight in paraformaldehyde and glutaraldehyde in a sodium cacodylate buffer, postfixed in osmium tetroxide in a sodium cacodylate buffer, dehydrated with a graded ethanol series, and critical point dried with carbon dioxide. The dried control and experimental model sections were mounted and imaged using a scanning electron microscope (SEM). Images of the trabecular meshwork were taken radially around the optic nerve at several magnifications. The images were segmented and analyzed using the ImageJ plug-in DiameterJ. Segmentation quality was confirmed using image subtraction of the original image from the segmented image and vice versa. All images had false positive and false negative total percentages of less than 5% of all pixels. Image overlays of the segmented and original images confirmed this qualitatively. This is an ongoing study. Image processing and analysis is still in progress. For a normal and an experimental animal, mean and median fiber bundle diameters were recorded for a total of 61 images. Thirty (49.1%) of the images analyzed were from the control sample and 31 (50.9%) were from the experimental sample. An analysis of variance found that both the mean (.52 μm) and median (.62 μm) fibrous bundle diameters were significantly different (ie, smaller) for the experimental model as compared to the mean (.73 μm) and median (.85 μm) fibrous bundle diameters for the control (p<.0001). The relationship between mean and median fibrous bundle diameters and sample (control or experimental) was not significantly correlated to radial location (relative to the optic nerve) or magnification of the image based on analyses of covariance. Thus far, this research demonstrates that in a swine model the fibrous bundles of the optic sheath’s connections to the optic nerve are statistically different (smaller) after exposure to increased ICP. We anticipate similar results from the other samples. Future research will evaluate if the thinning of fibrous bundles between optic sheath and optic nerve is caused by increased ICP. This will be accomplished by increasing the sample size and using other methods to induce increased ICP.

Polygenic risk score for predicting weight loss after bariatric surgery.

The extent of weight loss among patients undergoing bariatric surgery is highly variable. Herein, we tested the contribution of genetic background to such interindividual variability after biliopancreatic diversion with duodenal switch. Percentage of excess body weight loss (%EBWL) was monitored in 865 patients over a period of 48 months after bariatric surgery, and two polygenic risk scores were constructed with 186 and 11 (PRS186 and PRS11) single nucleotide polymorphisms previously associated with body mass index (BMI). The accuracy of the %EBWL logistic prediction model - including initial BMI, age, sex, and surgery modality, and assessed as the area under the receiver operating characteristics (ROC) curve adjusted for optimism (AUCadj = 0.867) - significantly increased after the inclusion of PRS186 (ΔAUCadj = 0.021; 95% CI of the difference [95% CIdiff] = 0.005-0.038) but not PRS11 (ΔAUCadj= 0.008; 95% CIdiff= -0.003-0.019). The overall fit of the longitudinal linear mixed model for %EBWL showed a significant increase after addition of PRS186 (-2 log-likelihood = 12.3; P = 0.002) and PRS11 (-2 log-likelihood = 9.9; P = 0.007). A significant interaction with postsurgery time was found for PRS186 (β = -0.003; P = 0.008) and PRS11 (β = -0.008; P = 0.03). The inclusion of PRS186 and PRS11 in the model improved the cost-effectiveness of bariatric surgery by reducing the percentage of false negatives from 20.4% to 10.9% and 10.2%, respectively. These results revealed that genetic background has a significant impact on weight loss after biliopancreatic diversion with duodenal switch. Likewise, the improvement in weight loss prediction after addition of polygenic risk scores is cost-effective, suggesting that genetic testing could potentially be used in the presurgical assessment of patients with severe obesity. Heart and Stroke Foundation of Canada (G-17-0016627) and Canada Research Chair in Genomics Applied to Nutrition and Metabolic Health (no. 950-231-580).

Community Endocrine Surgical Experience With False-Negative Afirma Gec® Results: 2011–2017

Afirma Gene Expression Classifier® (Afirma GEC) molecular analysis (Veracyte, Inc, San Francisco, CA) is a negative predictive value test developed to reduce the number of thyroidectomies in thyroid nodule patients with indeterminate cytology. GEC technology has reportedly reduced unnecessary thyroid surgery, but few studies have examined Afirma GEC false-negative rates, since usually patients with GEC benign nodules do not undergo surgery for definitive diagnosis. Occasionally, Afirma GEC benign patients require removal of their thyroid nodules for other reasons; this work describes the incidence of malignancy and noninvasive follicular thyroid neoplasms with papillary-like nuclear features (NIFTP) in this population. We reviewed our community endocrine surgical practice database for patients who had undergone thyroid surgery from January 2011 through April 2017 despite benign Afirma GEC results. Afirma GEC testing was completed for 475 patients during the study period. Surgery was clinically indicated for other reasons in 42 of the 193 patients (22%) with Afirma GEC benign results. Malignancy or NIFTP in the targeted nodule was found in the final histologic evaluation of 14 of the 42 Afirma GEC benign surgical patients. The Afirma GEC false-negative percentage for our incomplete surgical group (FNP-ISG), defined as the surgically proven false negatives divided by the total Afirma GEC benign patients, was 7.3%. Our high surgical rate in Afirma GEC benign nodules reveals an FNP-ISG of 7.3% in our community endocrine surgical patient population; this value exceeds the 5.7% reported in the multicenter 2012 Afirma GEC validation study. Afirma GEC = Afirma Gene Expression Classifier; FNA = fine-needle aspiration; FNP = false-negative percentage; FNP-ISG = false-negative percentage for an incomplete surgical group; NIFTP = noninvasive follicular thyroid neoplasms with papillary-like nuclear features.