Proportion Of False Positives Research Articles

Case Ascertainment in Pediatric Heart Failure Using International Classification of Disease Clinical Modification (ICD-CM) Codes.

Most epidemiological studies in pediatric heart failure (HF) use administrative database sources, defining patient cohorts by presence of a single HF ICD code. However, the ability of ICD codes to identify true HF patients is unknown in pediatrics.Here we describe the accuracy of HF ICD-10-CM code search algorithms, in identifying pediatric patients with HF from electronic data sources. Based on the adult HF literature, search algorithms were designed to incorporate HF ICD codes, imaging, and medications. Sensitivity, specificity, positive and negative predictive value and accuracy of the algorithms were tested among children in an advanced HF clinic ("Clinic cohort"). Top-performing algorithms were then tested in a large-scale regional electronic data warehouse (EDW), 01/2017 to 01/2020, generating the "EDW Cohort". False positive cases were identified and characterized by chart review. Within the Clinic Cohort, 78/378 patients (21%) had gold standard HF diagnoses. A search algorithm with one HF ICD coded visit was more sensitive but less specific than > 1 HF ICD coded visit, (sensitivity 94% and specificity 89% versus 69% and 97%, respectively). Correspondingly, > 1 ICD coded visit had a higher PPV than one ICD coded visit; 84% vs. 69%. Accuracy was similar (90% vs 91%). Presence of 1 HF ICD code combined with HF medication had high sensitivity, specificity, PPV, NPV and accuracy, all higher than the single ICD code algorithm. The "1 HF coded visit + any medication" algorithm resulted in highest accuracy (93%). Top-performing algorithms were tested in the EDW: the algorithm with > 1 HF ICD coded visit, and the algorithm with one HF ICD coded visit combined with HF medication. In the EDW Cohort, 133/248 (53.6%) patients had gold standard HF diagnoses though 115/248 (46.3%) were false positive cases; 41% of those had pulmonary over-circulation from congenital heart disease. Excluding children < 30days old and those with a history of an isolated VSD repair, complete AVSD repair, or PDA closure further reduced the proportion of false positives to 50/248 (20%). A search algorithm using a single HF ICD code can have acceptable sensitivity, specificity, PPV, NPV and accuracy in identifying children with HF from within electronic medical records. Similar to adult HF literature, specificity improves by including HF medication. When applied to large data sources, however, the search algorithms result in a high proportion of patients with pulmonary overcirculation related to congenital heart disease. To narrow the population to those with myocardial dysfunction, case identification may require use of ICD codes with linked of administrative, surgical, and/or imaging databases.

Coronary CTA and CT-FFR in trans-catheter aortic valve implantation candidates: a systematic review and meta-analysis

Abstract Objectives To perform a systematic review and meta-analysis on diagnostic accuracy and other outcomes of coronary computed tomography angiography (CCTA) and CT-derived fractional flow reserve (CT-FFR) in candidates for trans-catheter aortic valve implantation (TAVI). Background Screening for obstructive coronary artery disease (CAD) with CCTA during TAVI work-up could prevent unnecessary invasive coronary angiography (ICA). This becomes increasingly relevant as the TAVI-population grows. CT-FFR improves CCTA accuracy in chest pain patients, but its reliability is unknown in the TAVI-population. Methods Pubmed, Embase and Web of Science were searched for studies regarding CCTA and/or CT-FFR in TAVI-candidates. Primary endpoint was correct identification and rule-out of obstructive CAD. Results were pooled in a meta-analysis. Results Twenty-four of the 29 included articles were part of the meta-analysis, reporting results for CCTA and CT-FFR in 3.494 and 797 patients respectively. Reference standard was anatomical severity of CAD in most studies. At patient-level, pooled CCTA sensitivity was 94.3% and specificity 74.8%. CT-FFR sensitivity was 91.5% and specificity 60.3% with strong variation between studies. CCTA correctly ruled out CAD in 52% of all patients, but 42% of positive CCTAs were false positives. CT-FFR correctly ruled out CAD in 47% of all patients included in this meta-analysis, but showed better accuracy than CCTA in studies that compared both. In these studies, sensitivity of CCTA versus CT-FFR was 70.7% versus 79.0% and specificity was 64.4% versus 92.9%. Conclusions Negative CCTA effectively rules-out CAD in the TAVI-population. Adding CCTA to TAVI work-up could lead to significant reduction in pre-TAVI ICA procedures, but the proportion of false-positives remains high. In studies performing a direct comparison, CT-FFR performed better than CCTA. This is not reflected in the CT-FFR meta-analysis. Therefore, diagnostic accuracy of CT-FFR and added value over CCTA in the TAVI-population remains unclear. Further research regarding validation and reliability is warranted.Meta-analysis of CCTA and CT-FFR

Effect of Fluorescence Lymph Node Mapping on Improving Diagnostic Values of CT D3 Lymph Node Staging for Right-Sided Colon Cancer

Background/Objectives: This study evaluated the impact of fluorescence lymph node mapping (FLNM) using indocyanine green (ICG) on the diagnostic accuracy of preoperative computed tomography (CT) in right-sided colon cancer. Methods: A total of 218 patients who underwent laparoscopic right hemicolectomy with D3 lymph node dissection (LND) were analyzed: 86 patients in the FLNM group and 132 in the conventional surgery group. The FLNM technique allowed for enhanced intraoperative visualization of lymph node (LN) and more precise dissection, improving the identification of metastatic LNs. The diagnostic value of preoperative CT staging was assessed in both the FLNM and control groups by calculating the apparent prevalence, true prevalence, sensitivity, specificity, positive predictive value (PPV), negative predictive value, positive likelihood ratio (PLR), negative likelihood ratio, false positive and false negative proportions, and accuracy. Results: FLNM increased the accuracy of CT staging for detecting D3 LN metastasis in advanced cancer cases, with a higher PPV, PLR, and accuracy. In the FLNM group, the false-positive rate was significantly reduced, and the specificity was higher compared to the control group. Multivariate analysis identified FLNM as an independent factor associated with improved D3 LN metastasis detection. These findings suggest that incorporating FLNM into surgical procedures enhances the diagnostic value of preoperative CT by improving the precision of LND, particularly in patients with advanced colon cancer. Conclusions: The use of FLNM for D3 LND enhances the diagnostic accuracy of cN staging in right-sided colon cancer by improving surgical precision.

Evaluating the Modified American Academy of Pediatrics Screening Algorithm for Critical Congenital Heart Disease.

In 2018, an expert panel recommended two key modifications to the most used algorithm for screening neonates for critical congenital heart disease (CCHD). Our aim was to evaluate the outcomes of the modified algorithm compared with those of the original algorithm in a real-world setting. We compared the performance characteristics of an original CCHD algorithm used to screen term neonates at a large hospital system between October 26, 2018, and October 15, 2020, and the recommended modified algorithm used between October 15, 2020, and June 30, 2022. We calculated sensitivity, specificity, false positive rate, proportion of false positives with non-CCHD illness, and error rates of test administration and interpretation for each algorithm. Sensitivity was not significantly different between the modified algorithm compared with the original algorithm (40.00 vs. 12.50%, p ≥0.99), but specificity was lower (and hence false positive rate was higher) in the modified algorithm (99.91 vs. 99.98%, p < 0.001). Despite a higher false positive rate in the modified algorithm, the proportion of false positives with significant non-CCHD illness was similar (36.47 vs. 28.57%, p ≥0.99), a finding that translated to an increase in the number of cases of significant non-CCHD illness detected (11 cases out of 32,178 screens vs. 2 cases out of 32,984 screens). Error rates of test administration and interpretation were similar between the two algorithms. In this limited study, the modified algorithm for CCHD screening using pulse oximetry had a higher false positive rate than that of the original American Academy of Pediatrics algorithm. However, this higher rate led to an increased overall number of cases detected of significant non-CCHD illness. · Experts recommend two changes to the American Academy of Pediatrics-endorsed CCHD screen.. · This study evaluates the new algorithm for screening.. · The new algorithm detects at least as many cases as the original.. · There was no significant difference in test sensitivity for CCHD.. · The new algorithm has a statistically higher false positive rate..

Value of Functional Protein S Activity Assays for Detection of Type II Protein S Deficiency

Abstract Background Protein S deficiency is a hereditary thrombophilia which occurs in three forms: Type I (quantitative), type II (qualitative) and type III (qualitative due to a gain of function). Literature suggests that of these, type II is the rarest and most difficult to diagnose. Type II protein S deficiency is only detectable by assaying protein S activity with a functional, clot-based test. However, this test is susceptible to interference and has a lower specificity when compared to antigen assays used to detect free and total protein S levels. Subsequently, the Choosing Wisely Guidelines released by the ABIM Foundation recommends against using protein S activity assays entirely. Instead, they suggest using only free protein S antigen assays. Although the rationale is to avoid false positive results from the protein S activity assay, it ultimately may result in a failure to detect type II protein S deficiency. Objective This study, performed at a tertiary medical center in the Midwestern region of the United States, aims to determine the proportion of false positives in functional assays as well as the percent of true type II protein S deficiency cases within this testing population. Methods All cases with abnormal protein S function within the institution were evaluated from July 2018 through July 2023 and classified as acquired or hereditary deficiencies. Cases were classified as acquired if there was evidence of liver disease, vitamin K deficiency, ongoing thrombosis, consumption, disseminated intravascular coagulopathy, or pregnancy. Possible hereditary deficiency cases were classified as type I, type II and type III: Type I if all assays were low, type III if both active and free protein S assays were low with normal total protein S, and type II if only protein S activity was low. The cutoff defined for normal total and free protein S levels was greater than 70% activity, and the low protein S activity cutoff was defined as less than 50%. Patients with low protein S activity but normal immunologic protein S and an elevated factor VIII were considered falsely low values. Chart review will be performed for remaining cases with low protein S activity to rule out confounders such as vitamin K deficiency, warfarin use, acute phase reaction, or pregnancy. Results From a total of 8805 panels performed to assess for possible thrombophilia, 1580 (18%) were found to have decreased protein S activity. Of these cases, 599 (38%) were considered falsely decreased due to elevated factor VIII levels. 322 cases (3.7%) were considered possible hereditary deficiencies: 217 type III (2.5%), 27 type II (0.3%), and 80 type I (0.9%). Discussion Our data indicates the prevalence of subtypes may be differently distributed than previously believed, which may call for current guidelines to be reevaluated.

Gastric Cancer and Intestinal Metaplasia: Differential Metabolic Landscapes and New Pathways to Diagnosis.

Gastric cancer (GC) is the fifth most common cause of cancer-related death worldwide. Early detection is crucial for improving survival rates and treatment outcomes. However, accurate GC-specific biomarkers remain unknown. This study aimed to identify the metabolic differences between intestinal metaplasia (IM) and GC to determine the pathways involved in GC. A metabolic analysis of IM and tissue samples from 37 patients with GC was conducted using ultra-performance liquid chromatography with tandem mass spectrometry. Overall, 665 and 278 significant features were identified in the aqueous and 278 organic phases, respectively, using false discovery rate analysis, which controls the expected proportion of false positives among the significant results. sPLS-DA revealed a clear separation between IM and GC samples. Steroid hormone biosynthesis, tryptophan metabolism, purine metabolism, and arginine and proline metabolism were the most significantly altered pathways. The intensity of 11 metabolites, including N1, N2-diacetylspermine, creatine riboside, and N-formylkynurenine, showed significant elevation in more advanced GC. Based on pathway enrichment analysis and cancer stage-specific alterations, we identified six potential candidates as diagnostic biomarkers: aldosterone, N-formylkynurenine, guanosine triphosphate, arginine, S-adenosylmethioninamine, and creatine riboside. These metabolic differences between IM and GC provide valuable insights into gastric carcinogenesis. Further validation is needed to develop noninvasive diagnostic tools and targeted therapies to improve the outcomes of patients with GC.

The steatosis-associated fibrosis estimator (SAFE) outperformed the FIB-4 score in screening the population for liver disease

Introduction and ObjectivesAssessing fibrosis risk noninvasively is essential. The steatosis-associated fibrosis estimator (SAFE) score shows promise but needs validation. Patients and MethodsThis was a three-part study. In part 1, we compared the SAFE score with the Fibrosis-4 (FIB-4) and NAFLD fibrosis score (NFS) in the National Health and Nutrition Examination Survey (NHANES) cohort (2017–2020), using transient elastography (TE) as screening reference. In part 2, we examined patients who underwent liver biopsies at an Asian center between 2018 and 2020 to assess these models in various liver diseases. In part 3, the SAFE score was applied to adults in the NHANES cohort (1999–2016) to assess the correlation with mortality. ResultsIn part 1, we studied 6,677 patients, comprising 595 screening positive (TE ≥8 kPa). SAFE (cutoff 100) displayed a lower proportion of false positives (10.4 %) than FIB-4 (cutoff 1.3) and NFS (cutoff -1.455) (22.1 % and 43.6 %) while retaining a low proportion of false negatives (5.5 %). In part 2, SAFE outperformed FIB-4 (P = 0.04) and NFS (P = 0.04) in staging significant fibrosis (≥S2) in NAFLD and had similar accuracies in other etiologies. In part 3, the FIB-4, NFS, and SAFE score were associated with all-cause mortality in the general population, with c-statistics of 0.738, 0.736, and 0.759, respectively. ConclusionsThe SAFE score reduced futile referrals more effectively than FIB-4 without raising the missed TE ≥ 8 kPa rate. It correlated with all-cause mortality in the general population and excelled in staging significant fibrosis in NAFLD.

Reduction of false positives using zone-specific prostate-specific antigen density for prostate MRI-based biopsy decision strategies

ObjectivesTo develop and test zone-specific prostate-specific antigen density (sPSAD) combined with PI-RADS to guide prostate biopsy decision strategies (BDS).MethodsThis retrospective study included consecutive patients, who underwent prostate MRI and biopsy (01/2012–10/2018). The whole gland and transition zone (TZ) were segmented at MRI using a retrained deep learning system (DLS; nnU-Net) to calculate PSAD and sPSAD, respectively. Additionally, sPSAD and PI-RADS were combined in a BDS, and diagnostic performances to detect Grade Group ≥ 2 (GG ≥ 2) prostate cancer were compared. Patient-based cancer detection using sPSAD was assessed by bootstrapping with 1000 repetitions and reported as area under the curve (AUC). Clinical utility of the BDS was tested in the hold-out test set using decision curve analysis. Statistics included nonparametric DeLong test for AUCs and Fisher-Yates test for remaining performance metrics.ResultsA total of 1604 patients aged 67 (interquartile range, 61–73) with 48% GG ≥ 2 prevalence (774/1604) were evaluated. By employing DLS-based prostate and TZ volumes (DICE coefficients of 0.89 (95% confidence interval, 0.80–0.97) and 0.84 (0.70–0.99)), GG ≥ 2 detection using PSAD was inferior to sPSAD (AUC, 0.71 (0.68–0.74)/0.73 (0.70–0.76); p < 0.001). Combining PI-RADS with sPSAD, GG ≥ 2 detection specificity doubled from 18% (10–20%) to 43% (30–44%; p < 0.001) with similar sensitivity (93% (89–96%)/97% (94–99%); p = 0.052), when biopsies were taken in PI-RADS 4-5 and 3 only if sPSAD was ≥ 0.42 ng/mL/cc as compared to all PI-RADS 3-5 cases. Additionally, using the sPSAD-based BDS, false positives were reduced by 25% (123 (104–142)/165 (146–185); p < 0.001).ConclusionUsing sPSAD to guide biopsy decisions in PI-RADS 3 lesions can reduce false positives at MRI while maintaining high sensitivity for GG ≥ 2 cancers.Clinical relevance statementTransition zone-specific prostate-specific antigen density can improve the accuracy of prostate cancer detection compared to MRI assessments alone, by lowering false-positive cases without significantly missing men with ISUP GG ≥ 2 cancers.Key Points• Prostate biopsy decision strategies using PI-RADS at MRI are limited by a substantial proportion of false positives, not yielding grade group ≥ 2 prostate cancer.• PI-RADS combined with transition zone (TZ)-specific prostate-specific antigen density (PSAD) decreased the number of unproductive biopsies by 25% compared to PI-RADS only.• TZ-specific PSAD also improved the specificity of MRI-directed biopsies by 9% compared to the whole gland PSAD, while showing identical sensitivity.

Establishing a screening strategy for non-discriminatory reactive blood donors by constructing a predictive model of hepatitis B virus infection status from a single blood center in China.

When employing the transcription-mediated amplification method for screening blood donors, there are some non-discriminatory reactive results which are screening assay reactive but HBV-DNA discriminatory assay negative. This raises concerns regarding the possibility of false positives among donors, which may lead to permanent deferral of blood donors and affect blood supply. This study aimed to elucidate the infection status of these non-discriminatory reactive blood donors and develop and validate a model to predict individualized hepatitis B status to establish an optimal screening strategy. Supplementary tests were conducted on initial non-discriminating reactive donations to determine their HBV infection status, including repeat testing, viral load, serological marker detection, and follow-up. Primary clinical variables of the donors were recorded. Based on the Akaike information criterion, a stepwise forward algorithm was used to identify the predictive factors for information and construct a predictive model. The optimal screening strategy was determined through cost-effectiveness analysis. At the Blood Center of Zhejiang Province, 435 cases of initial non-discriminatory reactive donations were collected over two successive periods and sub-categorized through repeated testing into the following three groups: non-repeated positive group, non-discriminated positive group, and non-repeated HBV-DNA positive group. The HBV discriminatory rate increased after repeated testing (110/435, 25.29%). According to supplementary tests, the HBV-DNA positivity rate was 65.52% (285/435), and occult HBV infection was a significantly different among groups (χ2 = 93.22, p < 0.01). The HBV serological markers and viral load in the non-repeated positive group differed from those in the other two groups, with a lower viral load and a higher proportion of false positives. The predictive model constructed using a stepwise forward algorithm exhibited high discrimination, good fit, high calibration, and effectiveness. A cost-effectiveness analysis indicated that utilizing repeated discriminatory testing and the predictive model is an extremely beneficial screening approach for non-discriminatory reactive blood donors. Nearly two-third (65.52%) of the non-discriminatory reactive blood donors were HBV-DNA positive. Our innovative approach of constructing a predictive model as a supplementary screening strategy, combined with repeated discriminatory experiments, can effectively identify the infection status of non-discriminatory reactive blood donors, thereby increasing the safety of blood transfusions.

Calling Structural Variants with Confidence from Short-Read Data in Wild Bird Populations.

Comprehensive characterization of structural variation in natural populations has only become feasible in the last decade. To investigate the population genomic nature of structural variation, reproducible and high-confidence structural variation callsets are first required. We created a population-scale reference of the genome-wide landscape of structural variation across 33 Nordic house sparrows (Passer domesticus). To produce a consensus callset across all samples using short-read data, we compare heuristic-based quality filtering and visual curation (Samplot/PlotCritic and Samplot-ML) approaches. We demonstrate that curation of structural variants is important for reducing putative false positives and that the time invested in this step outweighs the potential costs of analyzing short-read-discovered structural variation data sets that include many potential false positives. We find that even a lenient manual curation strategy (e.g. applied by a single curator) can reduce the proportion of putative false positives by up to 80%, thus enriching the proportion of high-confidence variants. Crucially, in applying a lenient manual curation strategy with a single curator, nearly all (>99%) variants rejected as putative false positives were also classified as such by a more stringent curation strategy using three additional curators. Furthermore, variants rejected by manual curation failed to reflect the expected population structure from SNPs, whereas variants passing curation did. Combining heuristic-based quality filtering with rapid manual curation of structural variants in short-read data can therefore become a time- and cost-effective first step for functional and population genomic studies requiring high-confidence structural variation callsets.

FAOACA-SND: Fuzzy selfish node detection employing arithmetic optimization algorithm and cell automata in DTN

In recent years, the number of smart devices has grown exponentially. However, the security and privacy of these devices are often neglected, which can lead to serious consequences. In this paper, we propose a method for detecting selfish nodes in DTN. Our method is based on analyzing the behavior of nodes in the network and identifying those that are not following the protocol. Cluster read selection using the hybrid optimized algorithm and cellular automata is used as the first phase of selfish node detection. The second phase uses fuzzy logic to categorize the nodes as selfish or cooperative. Fuzzy logic receives four network parameters from the simulation in MATLAB in a dynamic environment. The four network parameters are packet drop, average delay, residual energy, and nodes' reputations. The simulation results depict that the proposed method is a very accurate way to find selfish nodes. In the proposed mechanism, the proportion of false positives has decreased by 61.53 % compared to the existing mechanism.

An Improved Big Data Analytics Architecture for Intruder Classification Using Machine Learning

The approval of retrieving information on the Internet originates several network securities matters. Intrusion recognition is a critical study in network security to spot unauthorized admission or occurrences on protected networks. Intrusion detection has a fully-fledged reputation in the current era. Research emphasizes several datasets to upsurge system precision and lessen the false-positive proportion. This article proposes a new intrusion detection system using big data analytics and deep learning to address some of the misuse and irregularity detection limitations. The proposed method could identify any odd activities in a network to recognize malicious or unauthorized action and permit a response during a confidentiality break. The proposed system utilizes the big data analytics platform based on parallel and distributed mechanisms. The parallel and distributed platforms improve the training time along with the accuracy. The experimentation appropriately classifies the information as either normal or abnormal. The proposed system has a recognition proportion of 96.11% that pointedly expands overall recognition accuracy related to existing strategies.

Clinical Acceptability of Artificial Intelligence-Screened Interstitial Lung Disease (AI-ILD) in Lung Cancer Patients Treated with Radiotherapy

Patients with interstitial lung disease (ILD) treated with thoracic radiotherapy (RT) are at greater risk of pulmonary toxicity. Automatic universal screening for ILD allows radiation oncologists (ROs) to risk stratify patients and implement necessary modifications to their respiratory monitoring or treatment. Automatic screening however may affect RO workload and so it is imperative to assess the clinical acceptability of this tool. We have developed a machine learning algorithm to identify patients who are at high risk of having ILD based on RT planning computed tomography (CT) images. A quality improvement (QI) project was initiated to test feasibility and acceptability of the machine learning algorithm. If positive, the results of the machine learning algorithm were made available to ROs via structured electronic reporting. ROs were prompted to review the patient and consider expert radiologist consultation if thought appropriate. All electronic surveys and qualitative comments were summarized to describe clinical acceptability. Expert radiologist established gold standard ILD status of all patients on the study. A formal review of RO feedback was collected for all screen-positive, true-positive cases. Two hundred forty cases were screened of which 45 were flagged as AI-ILD positive and the responsible RO notified. Of these 45 screen-positive cases, all continued on to RT except for 3 patients with tumor progression. From these 45, 24 surveys were completed, 21 had no prior suspicion of ILD. There were 7 true-positives, of which 1 had a survey response. Based on the survey responses, 88% of cases underwent review by the responsible RO. In 16 cases this automatic notification prompted case consultation with an expert radiologist. Expert review was performed from 10 minutes up to 53 hours after the email prompt to the radiologist, with median response time of 1.5 hours. In the 7 screen-positive, true-positive cases, only 2 were not previously known to the responsible RO. In the two cases where true-positive ILD status was previously unknown, one was a mild case of ILD and the other had previously received thoracic RT at this institution without ILD being identified, in both cases the ROs were grateful that this diagnosis was identified prior to treatment. RO confidence in the machine learning prediction was moderate due to the high proportion of false positives. Based on available survey results, more than 75% of the screen-positive cases were reviewed by the responsible RO and two-thirds of these involved expert radiology input. RO feedback was generally positive and this tool was rated as a net benefit despite the high rate of false-positives and the need for clarification.

A-088 Establish Laboratory Limit of Blank and Limit of Detection for LH Assay on the Ortho VITROS 3600 System

Abstract Background In our lab, Luteinizing hormone (LH) was tested on VITROS 3600 system with manufacture’s declared limit of detection (LoD) 0.216 mIU/mL, limit of Blank (LoB) 0.0367 mIU/mL. It’s determined with NCCLS guideline EP17, false positives (α) less than 5% and false negatives (β) less than 1%, based on more than 700 determinations on several systems. Chinese government launched industry standard WS/T 514-2017, defining how a clinical laboratory establish its detection capability with stated proportions α = β = 5% and some other differences from EP17. Here we set up the LoB, LoD of VITROS 3600 LH assay in our lab based on Chinese industry standard. Methods a) LoB was designed to run 4 blank samples, LoD with 4 low positive samples, respectively with two VITROS LH kit lots, on one VITROS 3600, over two runs per day, for 3–4 days using two different calibrations, therefore we got 120 results (outliers omitted) for LoB and LoD respectively. b) Negative (blank) samples were matrix fetal bovine serum (FBS), low positive samples were in house produced, VITROS 3600 System was well maintained, fully calibrated and quality controlled. c) Data analysis: LoB and LoD sample data are not Gaussian, according to WS/T 514-2017, the LoB and LoD were computed with nonparametric statistics. Results Perform steps described in method for each lot reagents. LoB data distribution were non-Gaussian (as when truncated at zero). therefore the LoB can be determined by ranking the data in order from lowest to highest concentration and using the following equations, LOB = Pct100-α, here α is 0.05, P is 0.95, 60 determinations, each reagent lot arrangement position = 0.5 + 60*0.95 = 57.5, result was not an integer, LOB should be re-calculated = X57 + 0.5(X57-X58) = 0.5(X57 + X58). The final result for LoB is 0.0105.with proportions α = β = 5%, each reagent lot LoD determinations more than 50, thereof, K-S test is used for normality analysis. All results of both reagent lots have P-value less than 0.05, which means that the samples were not been generated from a normal distribution. According to WS/T 514-2017, the LoD is estimated with non-parametric statistical derivation method. All results are greater than LoB 0.0105, 0% results are lower than LoB which is less than stated probability 5%. The median value for lot 1990 is 0.0898, and lot 2010 0.0899, so LoD is 0.0899. Conclusion The limit of detection (LoD) for VITROS LH assay in our lab is 0.0899 mIU/mL, the limit of Blank (LoB) is 0.0105 mIU/mL, determined with WS/T 514-2017, and with proportions of false positives(α) = false negatives (β) = 5%. In our lab the detection capability is surpass to what manufacture claimed in the IFU. It could be as following factors: singleton platform in our lab, with greater stated proportions, less determinations, and consistently assigned operator. So we can conclude that this system is fit for a more sensitive detection scenario, like pediatric patients.

Catégorisation de la densité mammaire et dépistage organisé du cancer du sein

The objective of this study was to evaluate the intra- and inter-rater agreement of radiologists regarding the evaluation of breast density. Breast density assessments of 120 cases were performed by four radiologists in the city of Ouagadougou according to the fifth edition of the American College of Radiology BI-RADS atlas. Cohen's weighted kappa coefficients and Fleiss kappa coefficients were used to estimate agreement between observers and with a panel of three experts radiologists. A new evaluation of the 120 cases was performed by all raters one month after the initial evaluation. Inter-rater kappa coefficients ranged from 0.55 to 0.74. The Fleiss kappa coefficient was 0.58, 0.43, 0.41, and 0.43 for categories A, B, C, and D respectively. In terms of classification into "sparse breasts" and "dense breasts", the kappa coefficients ranged from 0.47 to 0.67. Taking the results of the expert panel as a reference, the proportion of false positives in the diagnosis "dense breasts" ranged from 18.6% to 26.8%. Intraobserver agreement was good. Our study showed moderate to good intra- and inter-raters agreements. Upgrading and harmonisation of practices will be used to empower radiologists to participate in organised breast cancer screening in Burkina Faso.

Assessment of optometrists' referral accuracy and contributing factors: A review.

In the UK, ophthalmology has the highest number of outpatient appointments within the National Health Service. False-positive referrals from primary care are one of the main factors contributing to the oversubscription of hospital eye services (HESs). We reviewed the accuracy of referrals originating from primary care optometrists and contributing factors, such as condition type and years since registration. Of the 31 studies included in the review, 22 were retrospective analyses of referrals and appointments at the HES. Eight were prospective studies, and one used online clinical vignettes. Seven assessed the accuracy of referrals for all ocular conditions. The remaining studies focused on glaucoma (n = 11), cataracts (n = 7), emergency conditions (n = 4), neovascular age-related macular degeneration (n = 1) and paediatric binocular vision (n = 1). The diagnostic agreement for suspected emergency ocular conditions was the lowest, with only 21.1% of referrals considered to require urgent attention in one study. For glaucoma, the first-visit discharge rate was high (16.7%-48%). Optometrist referral accuracy was overall 18.6% higher than General Medical Practitioners'; however, the two mainly referred different ocular conditions. Female optometrists made more false-positive referrals than males (p = 0.008). The proportion of false positives decreased by 6.2% per year since registration (p < 0.001). There was significant variation in referral accuracy across different ocular conditions, partly due to differences when defining accurate referrals. Optometrists working in primary care are generally more limited in their resources than the HES. Thus, choosing the cautious option of referral when they are unsure could be in the patients' best interests. The possible effect of increased use of advanced imaging on referrals requires evaluation. Although interventions such as refinement schemes have been put in place, these vary across regions, and their approaches such as virtual referral triaging may reduce unnecessary HES face-to-face appointments and promote communication between primary and secondary care.

Optimizing efficiency of a custom clinical decision support tool improves adult congenital heart disease care

Study objectiveImprove the efficiency of an inpatient clinical decision support tool (CDS) for patients with adult congenital heart disease (ACHD). DesignThe efficiency of a CDS was evaluated across two time periods and compared. SettingAn academic, tertiary care center. ParticipantsACHD patients roomed in an inpatient setting. InterventionPlan-Do-Study-Act (PDSA) methods were applied starting in 2021 and included refinement of diagnostic codes and the addition of department encounter codes. Main outcome measuresTrue positive and false positive CDS alerts. ResultsBaseline data from 2017 had a median (IQR) of 38 (17) and 2019 baseline data had 65 (19) total alerts per month. Combining both baseline data years, the median true positive CDS alerts was 47.3 %. There were 71 (6) total alerts per month for the 2021–2022 time period and with ongoing PDSA cycles and optimization in the CDS the true positive alerts improved substantially resulting in a shifting of the median to 78.9 % within 9 months. ConclusionCDS can efficiently notify providers when an ACHD patient is encountered. The use of ICD 10 codes alone to identify ACHD patients has limited accuracy with a high proportion of false positives. Ongoing revision of the CDS system methods is important to improving efficiency and minimizing provider alert fatigue.

Removing Human Bottlenecks in Bird Classification Using Camera Trap Images and Deep Learning

Birds are important indicators for monitoring both biodiversity and habitat health; they also play a crucial role in ecosystem management. Declines in bird populations can result in reduced ecosystem services, including seed dispersal, pollination and pest control. Accurate and long-term monitoring of birds to identify species of concern while measuring the success of conservation interventions is essential for ecologists. However, monitoring is time-consuming, costly and often difficult to manage over long durations and at meaningfully large spatial scales. Technology such as camera traps, acoustic monitors and drones provide methods for non-invasive monitoring. There are two main problems with using camera traps for monitoring: (a) cameras generate many images, making it difficult to process and analyse the data in a timely manner; and (b) the high proportion of false positives hinders the processing and analysis for reporting. In this paper, we outline an approach for overcoming these issues by utilising deep learning for real-time classification of bird species and automated removal of false positives in camera trap data. Images are classified in real-time using a Faster-RCNN architecture. Images are transmitted over 3/4G cameras and processed using Graphical Processing Units (GPUs) to provide conservationists with key detection metrics, thereby removing the requirement for manual observations. Our models achieved an average sensitivity of 88.79%, a specificity of 98.16% and accuracy of 96.71%. This demonstrates the effectiveness of using deep learning for automatic bird monitoring.

Auditory Screening in Newborns after Maternal SARS-CoV-2 Infection: An Overview.

Several viruses have previously been reported to be responsible for congenital hearing loss; therefore, since the beginning of the SARS-CoV-2 infection pandemic, various reports have investigated a possible link. The aim of this review is to assess the possible link between maternal COVID-19 infection and congenital hearing loss. This systematic review was performed using PRISMA criteria, searching Medline and Embase databases from March 2020 to February 2023. A total of 924 candidate papers were identified; however, considering the specific selection criteria, only nine were selected for additional analysis. The overall number of children born from mothers infected with COVID-19 during pregnancy identified through this review was 1687. The confirmed cases of hearing loss were 0.7% (12/1688); a description of its nature (sensorineural vs. conductive) is missing in the selected studies, and the follow-up period is variable across the analyzed papers. Surprisingly, a large proportion of false positives were recorded at the first stage of screening, which resulted normal at the re-test. Currently, a correlation between congenital hearing loss and SARS-CoV-2 infection cannot be definitively established. Further studies are desirable to provide additional evidence on this topic.

Using a handheld metal detector to detect ingested hooks and other metallic objects in freshwater turtles

AbstractTurtles can be unintentionally hooked during recreational and commercial fishing operations. While hook ingestion is not always fatal, ingested hooks can harm or kill turtles by damaging soft tissues or affecting digestion. Detecting ingested hooks and other metallic debris in the field is a challenge. As such, we tested the efficacy of a handheld metal detector in detecting ingested fish hooks and incidental metallic debris in freshwater turtles in Georgia, USA. We captured 426 individual freshwater turtles representing 4 species and radiographed 106 yellow‐bellied sliders (Trachemys scripta scripta). Of 37 turtles that tested positive with a handheld metal detector, radiographs revealed 17 (46%) had ingested at least one hook, 15 (40%) had ingested an unidentifiable metal object or objects, and 5 (14%) showed no clear signs of hooks or other metallic objects. All 69 turtles that tested negative in the field and were radiographed showed no signs of metal ingestion. Accordingly, our proportion of false positives was 14% (5 of 37), false negatives (metal not detected in field but on follow‐up radiograph) were 0% (0 of 69), and our overall accuracy at detecting ingested metal with a handheld metal detector was 95% (101 of 106 radiographed turtles). Thus, handheld metal detectors may be a useful tool for detecting ingested hooks and other metallic objects in freshwater turtles.