A generalized-weights solution to sample overlap in meta-analysis.

To identify radiologists' characteristics associated with interpretive performance in screening mammography. The study was approved by institutional review boards of University of Washington (Seattle, Wash) and institutions at seven Breast Cancer Surveillance Consortium sites, informed consent was obtained, and procedures were HIPAA compliant. Radiologists who interpreted mammograms in seven U.S. regions completed a self-administered mailed survey; information on demographics, practice type, and experience in and perceptions of general radiology and breast imaging was collected. Survey data were linked to data on screening mammograms the radiologists interpreted between January 1, 1998, and December 31, 2005, and included patient risk factors, Breast Imaging Reporting and Data System assessment, and follow-up breast cancer data. The survey was returned by 71% (257 of 364) of radiologists; in 56% (205 of 364) of the eligible radiologists, complete data on screening mammograms during the study period were provided; these data were used in the final analysis. An evaluation of whether the radiologists' characteristics were associated with recall rate, false-positive rate, sensitivity, or positive predictive value of recall (PPV(1)) of the screening examinations was performed with logistic regression models that were adjusted for patients' characteristics and radiologist-specific random effects. Study radiologists interpreted 1 036 155 screening mammograms; 4961 breast cancers were detected. Median percentages and interquartile ranges, respectively, were as follows: recall rate, 9.3% and 6.3%-13.2%; false-positive rate, 8.9% and 5.9%-12.8%; sensitivity, 83.8% and 74.5%-92.3%; and PPV(1), 4.0% and 2.6%-5.9%. Wide variability in sensitivity was noted, even among radiologists with similar false-positive rates. In adjusted regression models, female radiologists or fellowship-trained radiologists had significantly higher recall and false-positive rates (P < .05, all). Fellowship training in breast imaging was the only characteristic significantly associated with improved sensitivity (odds ratio, 2.32; 95% confidence interval: 1.42, 3.80; P < .001) and the overall accuracy parameter (odds ratio, 1.61; 95% confidence interval: 1.05, 2.45; P = .028). Fellowship training in breast imaging may lead to improved cancer detection, but it is associated with higher false-positive rates.

Value of Inpatient Inherited Thrombophilia Testing Following Acute VTE: A 3-Year Retrospective Study

The accuracy of mammography reading varies among radiologists. We conducted a population-based assessment on radiologist variation in false- positive rates of screening mammography and its associated radiologist characteristics. About 27,394 screening mammograms interpreted by 1067 radiologists were identified from a 5% non-cancer sample of Medicare claims during 1998-1999. The data were linked to the American Medical Association Masterfile to obtain radiologist characteristics. Multilevel logistic regression models were used to examine the radiologist variation in false-positive rates of screening mammography and the associated radiologist characteristics. Radiologists varied substantially in the false-positive rates of screening mammography (ranging from 1.5 to 24.1%, adjusting for patient characteristics). A longer time period since graduation is associated with lower false-positive rates (odds ratio [OR] for every 10 years increase: 0.87, 95% Confidence Interval [CI], 0.81-0.94) and female radiologists had higher false-positive rates than male radiologists (OR = 1.25, 95% CI, 1.05-1.49), adjusting for patient and other radiologist characteristics. The unmeasured factors contributed to about 90% of the between-radiologist variance. Radiologists varied greatly in accuracy of mammography reading. Female and more recently trained radiologists had higher false-positive rates. The variation among radiologists was largely due to unmeasured factors, especially unmeasured radiologist factors. If our results are confirmed in further studies, they suggest that system-level interventions would be required to reduce variation in mammography interpretation.

High False Positive Rate with the Use of CT and FDG-PET in Post-Remission Surveillance for Hodgkin Lymphoma.

Suicide is an important public health problem and prominent among U.S. Veterans and active duty troops. Prediction of suicide and suicide attempts is problematic because these are low-frequency events and traditional clinical screening approaches have a high false positive rate. Large clinical databases extracted from electronic health records permit study of suicidal behavior in larger populations than previously possible using sampling techniques. In addition to offering structured data, text search and classification methods can identify additional risk variables. Data extracted from clinical records of 250,000 veterans were modeled using machine learning methodology. To predict suicide attempts in this population over a 10 year period. In contrast to previously reported models, our results showed high specificity and a false positive rate of 0.5%, contrasting with other studies showing false positive rates exceeding 20%. The model showed lower specificity with a true positive rate of 27% and a false negative rate of 73%. These results suggest that a machine learning approach developed with large data sets can usefully supplement current approaches to prediction of suicidal behavior.

Background: Tests that purport to measure malingering such as the Structured Inventory of Malingered Symptomatology (SIMS) are associated with a risk to the public.The magnitude of this risk can be operationalized as the frequency of false positives, i.e., proportion of persons classified as malingerers and thus denied therapy and other medical benefits.Method: This review deals with the outcomes of studies of content, divergent, and criterion validity of the SIMS.We calculated an average risk to the public caused by the rates of false positives in published SIMS data on several clinical groups: psychiatric patients, survivors of high impact motor vehicle accidents (MVAs), and trauma-exposed war veterans. Results:(1) Content analyses demonstrated that almost all SIMS items describe medical symptoms, but these are fallaciously scored by the SIMS as indicative of malingering.(2) Calculations of divergent validity suggest that the SIMS measures the presence of medical symptoms rather than their malingering.For instance, SIMS total score correlates positively and highly with PCL-5 measure of PTSD (r=.60).The SIMS Amnestic Disorder scale correlates positively with Rivermead measure of post-concussive symptoms (r=.42).The SIMS Neurological Impairment scale correlates positively with neuropsychological symptoms measured by Post-MVA Neurological Symptoms (PMNS) scale (r=.41).(3) Criterion validity results of a recent meta-analysis indicated no significant capacity of the SIMS to differentiate legitimate patients from malingerers.Furthermore, published SIMS data indicate extremely high rates of false positives: 82.7% of US veterans with PTSD, 78.3% of patients injured in high impact MVAs, and 72.0% of legitimate psychiatric inpatients.An average risk to the public (i.e., the risk for genuine medical patients to be falsely classified as malingering) as suggested by the weighted mean for these SIMS data is 78.8%.Patients with more medical symptoms are significantly more likely to be fallaciously classified by their SIMS scores as "malingerers" than their less symptomatic counterparts. Discussion and Conclusions:The SIMS is a fatally flawed psychological test with alarmingly high iatrogenic rates.Its use constitutes malpractice.

Malware detection engines are required a high True Positive Rate (TPR) and low False Positive rate (FPR). For example, to obtain the VB100 certification conducted by Virus Bulletin in the U.K., the detector's TPR must be 99.5% or higher, and FPR less than 0.01%. However, it is difficult for signature-based malware detection engines to detect zero-day or new variants of malware. For such malware, an approach based on machine learning is considered effective because the features of specimens are analyzed in a complex manner. Therefore, we created a malware detection model from surface analysis logs, and PE header dumps by machine learning using the FFRI Dataset 2018. Furthermore, we verified the accuracy under the constraint that the FPR is less than a certain value. As a result, we succeeded in creating a new model with high accuracy: when FPR is set to be less than 1%, TPR is 99.7%, when FPR is set to be less than 0.1%, TPR is 98.7%, and when FPR is set to be less than 0.01% and TPR is 94.5%. In addition, we revealed features with high contribution to malware detection in this model.

To evaluate the accuracy of diagnosing N3 disease using positron emission tomography (PET) with 2-[fluorine-18]fluoro-2-deoxy-D-glucose (FDG) in patients with pulmonary disease. Twenty patients diagnosed as FDG-PET N3 were enrolled. On FDG-PET, lymph nodes were considered to be positive when increased uptake as compared with that of the surrounding mediastinum was visually observed, or the mean standardized uptake ratio (SUR) was more than 2, 2.5, or 3. On CT, lymph nodes exceeding 1 cm in the shortest diameter were regarded as positive. The PET result was true positive (TP) in 2 patients and false positive (FP) in 18 with an overall accuracy (OA) of 10% using visual criteria. Using an SUR of more than 2.5, the result was TP in 2, FP in 3, and true negative (TN) in 15, the false negative (FN) in 0, with an OA of 85%. CT diagnosis was TP in 2, FP in 9, and TN in 9 with an OA of 55%. The accuracy using the SUR criteria of more than 2.5 was superior to that of CT. Of 20 patients with the diagnosis of PET N3, we found frequent over-diagnosis in nodal staging using the visual criteria.

A panel of seven protein tumour markers for effective and affordable multi-cancer early detection by artificial intelligence: a large-scale and multicentre case–control study

The raw fMRI data are normally analyzed by statistical methods (Student's t or non-parametric tests) to yield measures of physiological responses. This analysis assumes that successive events in the raw signal are independent (white noise) 1 and hence have a Gaussian distribution. Colored noise if present in the dynamic fMRI data can cause high rate of false positives 2. Such positive correlations have been reported in human fMRI data 1, 2 which are characterized by a spectrum with increasing power toward the low frequencies (1/f pattern). This 1/f pattern can be numerically characterized by a fractal parameter, the Hurst exponent, H. The fMRI experiments of the a-chloralose (40 mg/kg/hr) anesthetized rats (n=12) were conducted on a 4.0 T or 9.4 T spectrometer using a 1 H resonator/surface-coil radio-frequency probe 3 and echo-planar imaging (EPI). Gradient-echo EPI data were acquired with repetition time of 200 ms in matrices of 3232 (4.0 T) and 6464 (9.4 T). Data were collected in vivo and post mortem. Scaled windowed variance (SWV) method 4 was used to calculate the fractal parameter, H and the nearest neighbor correlation coefficient, r1 5 from the fMRI time series. The length of the time series, 4096 scans (4.0 T) and 8192 scans (9.4 T), were adequate for a reliable estimation of H 6. The fMRI data from the 4.0T magnet was dominated by white noise with H=0.530.014. All 9.4 T fMRI time series showed fractional Gaussian noise (fGn) with H>0.5, different from white noise. The H of fGn within its lower and upper bounds of 0 and 1 characterizes the correlation structure itself: with H=0.5 the signal is random, H 0.5 indicates correlation with positive r1 6. Representative Hurst exponent maps obtained in vivo and post mortem were obtained for both 4.0 T and 9.4 T. In vivo cortical and subcortical gray matter regions exhibited much higher values of H (0.730.05) with r1=0.38 than white matter regions (H=0.570.01, r1=0.10). The post mortem results showed a significant (p<0.05) decrease in fractal structure obtained in both gray and white matter regions (H=0.610.03 and H=0.560.03, respectively) and the fractal or colored noise correlation structures were mainly broken in the gray matter regions (r1=0.17). High magnetic field fMRI data reveal fractal patterns across different brain regions. These findings should have implications in fMRI data processing: successive events of the fMRI signal are correlated hence statistical mapping should be used with prior treatment of data, such as creating surrogate time series, which destroys the correlation structure in a time series without the modification of actual values 7.

In noninferiority studies, a limit of indifference is used to express a tolerance in results such that the clinician would regard such results as being acceptable or 'not worse'. We applied this concept to a measure of accuracy, the Receiver Operating Characteristic (ROC) curve, for a sequence of tests. We expressed a limit of indifference for the range of acceptable sensitivity values and examined the associated cost of testing within this range. In doing so, we generated the minimum cost maximum ROC (MCMROC) curve, which reflects the reduced sensitivity and cost of testing. We compared the MCMROC and its associated cost curve between limits of indifference set to 0.999 [a 0.1% reduction in true positive rate (TPR)], 0.95 (a 5% reduction in TPR), and 1 (no reduction in TPR). The limit of indifference tended to have less of an effect on the MCMROC curves than on the associated cost curves that were greatly affected. Cost was reduced at high false positive rates (FPRs) at higher limit of indifference (0.999) and at small FPRs as the limit of indifference decreased (0.95). These patterns were also observed as applied to sequential strategies used to diagnose diabetes in the Pima Indians.

Identity management (IdM) systems employ Identity Providers (IdPs), as guardians of users' critical information. However, Distributed Denial-of-Service (DDoS) attacks can make IdPs operations unavailable, compromising legitimate users. In the literature, the main countermeasures against DDoS attacks are based on either the application of external resources to extend the system lifetime (replication) or on the DDoS attacks detection. The first approach increases the solutions cost, and in general the second one is prone to high rates of false negatives and/or false positives. This work presents SAMOS, a first scheme to mitigate DDoS attacks in IdM systems through a novel approach: organizations of IdP clustering using optimization techniques. SAMOS is started based on the monitoring of processing and memory resources, differently from the solutions in the literature that are started based on the attack detection by the network traffic analysis. SAMOS minimizes the DDoS attacks effects using operational IdPs in the system, differently from the works that employ external computer resources. Results considering data from real IdM systems indicate the scheme viability.

Large variability in thresholds to sensory stimuli is observed frequently even in healthy populations. Much of this variability is attributed to genetics and day-to-day fluctuation in sensitivity. However, false positives are also contributing to the variability seen in these tests. In this study, random number generation was used to simulate responses in threshold methods using different "stopping rules": ascending 2-alternative forced choice (AFC) with 5 correct responses; ascending 3-AFC with 3 or 4 correct responses; staircase 2-AFC with 1 incorrect up and 2 incorrect down, as well as 1 up 4 down and 5 or 7 reversals; staircase 3-AFC with 1 up 2 down and 5 or 7 reversals. Formulas are presented for rates of false positives in the ascending methods, and curves were generated for the staircase methods. Overall, the staircase methods generally had lower false positive rates, but these methods were influenced even more by number of presentations than ascending methods. Generally, the high rates of error in all these methods should encourage researchers to conduct multiple tests per individual and/or select a method that can correct for false positives, such as fitting a logistic curve to a range of responses.

This chapter, per the authors, explains the inherent impossibility of “AI detection,” and explores the material and psychological impacts of AI detection false positives on students. A small corpus study is presented demonstrating much higher than advertised rates of false positives across a range of popular “AI detection” tools. Based on this study along with news reports and first-person testimony from affected students, the chapter presents the possibility that neurodivergent writers, along with L2 writers, are more likely to be impacted by false positives. Given the current rates of mental health challenges on college campuses and the likelihood of a disproportionate impact on students who already face marginalization, the use of these AI detection tools is argued to be unethical. The chapter closes with recommendations for writing teachers.

Introduction This study aims to explore the relationship between sleep quality and duration and the recovery outcomes of hospitalized patients. Data was sourced from the National Health and Nutrition Examination Survey (NHANES) for the years 2005-2020. Methods Seven NHANES datasets(2005-2006, 2007-2008, 2009-2010, 2011-2012, 2013-2014, 2015-2016, and 2017-2020) were compiled, focusing on sleep disorders, hospital utilization, medical conditions, body measures, and demographics. The data was pre-processed using Python, filtering study IDs to ensure consistency across datasets. The final dataset included 931 rows and 19 columns after excluding incomplete responses and focusing on patients with overnight hospital stays. Recovery was defined as a binary variable based on self-reported general health condition, categorized as “Recovered” (Good, Very Good, Excellent) or “Not Recovered” (Poor, Fair). Results Logistic regression was employed to assess the impact of average sleep duration on recovery. An initial model using only sleep duration as the predictor showed an accuracy of 56.68%. A more comprehensive model incorporating additional variables (e.g., sleep quality, number of hospital nights, age, ethnicity) improved accuracy to 66.31%. However, this model exhibited a high false positive rate, indicating it often incorrectly predicted recovery. The model’s sensitivity (true positive rate) was 86.8%, accurately predicting recovery in 86.8% of cases within the test data. Specificity (true negative rate) was 39.5%, indicating a high rate of false positives. Key predictors identified through recursive feature elimination included sleep quality, number of hospital nights, age, and ethnicity. Average sleep duration did not significantly impact recovery (p = 0.66). Conclusion The study found that while sleep quality and certain demographic factors significantly influence recovery, average sleep duration alone is not a strong predictor. The logistic regression model requires further refinement to reduce false positives and improve specificity before it can be effectively used in clinical practice. Support (if any) None