Bovine brucellosis, primarily caused by Brucella abortus, severely affects both animal health and human well-being. Accurate diagnosis is crucial for designing informed control and prevention measures. Lacking a gold standard test makes it challenging to determine optimal cut-off values and evaluate the diagnostic performance of tests. In this study, we developed a novel Bayesian Latent Class Model that integrates both binary and continuous testing outcomes, incorporating additional fixed (parity) and random (farm) effects, to calibrate optimal cut-off values by maximizing Youden Index. We tested 651 serum samples collected from six dairy farms in two regions of Henan Province, China with four serological tests: Rose Bengal Test, Serum Agglutination Test, Fluorescence Polarization Assay, and Competitive Enzyme-Linked Immunosorbent Assay. Our analysis revealed that the optimal cut-off values for FPA and C-ELISA were 94.2 mP and 0.403 PI, respectively. Sensitivity estimates for the four tests ranged from 69.7% to 89.9%, while specificity estimates varied between 97.1% and 99.6%. The true prevalences in the two study regions in Henan province were 4.7% and 30.3%. Parity-specific odds ratios for positive serological status ranged from 1.2 to 2.2 for different parity groups compared to primiparous cows. This approach provides a robust framework for validating diagnostic tests for both continuous and discrete tests in the absence of a gold standard test. Our findings can enhance our ability to design targeted disease detection strategies and implement effective control measures for brucellosis in Chinese dairy farms.

OBJECTIVE: Being able to correctly interpret radiographs after a traumatic dental injury is an essential skill for providing appropriate and timely treatment. The aim of this study was to assess the impact of case complexity on paediatric dentists\' performance when radiographically diagnosing traumatic dental injuries (TDI) and to investigate a possible added value of cone-beam computed tomography (CBCT) when compared with digital intra-oral radiography (2D vs 3D).
METHODS: A test panel of paediatric dentists was instructed to detect, identify and interpret radiographic findings using either 2D or 3D images. Intra-oral radiographs and CBCT images of 20 trauma cases were presented in random order, and the findings were recorded using structured scoring sheets. Case complexity was determined by two experienced benchmark scorers. Results were analysed using generalized linear mixed modelling.
RESULTS: In general, performance for detection, identification and interpretation of findings was low, both with 2D and 3D images, with significantly lower values for difficult cases (p < 0.05). For easy as well as for difficult cases, 3D imaging resulted in a significantly better performance for detection and identification of findings (P < 0.001). This was not the case for correct interpretation, where significantly poorer performance was seen when using 3D images for difficult cases (p < 0.05).
CONCLUSIONS: This study provides evidence that case complexity of traumatic dental injuries influences diagnostic performance. The use of CBCT enhanced detection and identification of findings but when case complexity increased, 3D imaging adversely affected correct interpretation.

BACKGROUND: It is unclear whether the Japan Narrow-Band Imaging Expert Team (JNET) classification and pit pattern classification are applicable for diagnosing neoplastic lesions in patients with ulcerative colitis (UC).
OBJECTIVE: To clarify the diagnostic performance of these classifications for neoplastic lesions in patients with UC.
METHODS: This study was conducted as a single-center, retrospective case-control study. Twenty-one lesions in 19 patients with UC-associated neoplasms (UCAN) and 23 lesions in 22 UC patients with sporadic neoplasms (SN), evaluated by magnifying image-enhanced endoscopy, were retrospectively and separately assessed by six endoscopists (three experts, three non-experts), using the JNET and pit pattern classifications. The results were compared with the pathological diagnoses to evaluate the diagnostic performance. Inter- and intra-observer agreements were calculated.
RESULTS: In this study, JNET type 2A and pit pattern type III/IV were used as indicators of low-grade dysplasia, JNET type 2B and pit pattern type VI low irregularity were used as indicators of high-grade dysplasia to shallow submucosal invasive carcinoma, JNET type 3 and pit pattern type VI high irregularity/VN were used as indicators of deep submucosal invasive carcinoma. In the UCAN group, JNET type 2A and pit pattern type III/IV had a low positive predictive value (PPV; 50.0% and 40.0%, respectively); however, they had a high negative predictive value (NPV; 94.7% and 100%, respectively). Conversely, in the SN group, JNET type 2A and pit pattern type III/IV had a high PPV (100% for both) but a low NPV (63.6% and 77.8%, respectively). In both groups, JNET type 3 and pit pattern type VI-high irregularity/VN showed high specificity. The inter-observer agreement of JNET classification and pit pattern classification for UCAN among experts were 0.401 and 0.364, in the same manner for SN, 0.666 and 0.597, respectively. The intra-observer agreements of JNET classification and pit pattern classification for UCAN among experts were 0.387, 0.454, for SN, 0.803 and 0.567, respectively.
CONCLUSIONS: The accuracy of endoscopic diagnosis using both classifications was lower for UCAN than for SN. Endoscopic diagnosis of UCAN tended to be underestimated compared with the pathological results.

OBJECTIVE: Research on implementation of artificial intelligence (AI) in radiology workflows and its impact on reports remains scarce. In this study, we aim to assess if an AI platform would perform better than clinical radiology reports in evaluating noncontrast chest computed tomography (CT) scans.
METHODS: Consecutive patients who had undergone noncontrast chest CT were retrospectively identified. The radiology reports were reviewed in a binary fashion for reporting of pulmonary lesions, pulmonary emphysema, aortic dilatation, coronary artery calcifications (CAC), and vertebral compression fractures (VCF). CT scans were then processed using an AI platform. The reports\' findings and the AI results were subsequently compared to a consensus read by two board-certificated radiologists as reference.
RESULTS: A total of 100 patients (mean age: 64.2 ± 14.8 years; 57% males) were included in this study. Aortic segmentation and calcium quantification failed to be processed by AI in 2 and 3 cases, respectively. AI showed superior diagnostic performance in identifying aortic dilatation (AI: sensitivity: 96.3%, specificity: 81.4%, AUC: 0.89) vs (Reports: sensitivity: 25.9%, specificity: 100%, AUC: 0.63), p <0.001; and CAC (AI: sensitivity: 89.8%, specificity: 100, AUC: 0.95) vs (Reports: sensitivity: 75.4%, specificity: 94.9%, AUC: 0.85), p = 0.005. Reports had better performance than AI in identifying pulmonary lesions (Reports: sensitivity: 97.6%, specificity: 100%, AUC: 0.99) vs (AI: sensitivity: 92.8%, specificity: 82.4%, AUC: 0.88), p = 0.024; and VCF (Reports: sensitivity:100%, specificity: 100%, AUC: 1.0) vs (AI: sensitivity: 100%, specificity: 63.7%, AUC: 0.82), p <0.001. A comparable diagnostic performance was noted in identifying pulmonary emphysema on AI (sensitivity: 80.6%, specificity: 66.7%. AUC: 0.74) and reports (sensitivity: 74.2%, specificity: 97.1%, AUC: 0.86), p = 0.064.
CONCLUSIONS: Our results demonstrate that incorporating AI support platforms into radiology workflows can provide significant added value to clinical radiology reporting.

MRI plays an important role in the diagnosis and surgical planning of pelvic endometriosis (PE), and imaging reports should contain all relevant information (completeness). As structured reports are being increasingly utilized, we aimed to evaluate whether structured MRI reporting increases the quality of reports regarding completeness and, consequently, their perceived value by gynecologists, in comparison to free-text reports. We also aimed to compare the diagnostic performance of both formats.
We retrospectively included 28 consecutive women with histologically proven PE who underwent MRI within one month before surgery. Two abdominal radiologists (Rd1/Rd2, 3y/12y experience), blinded to clinical and surgical data, individually elaborated free-text reports and, four months later, structured reports. Completeness (defined as description of six key anatomical sites deemed essential for surgical planning in a consensus of four-blinded external experts) and diagnostic performance (sensitivity and specificity) by site (histology as reference) were compared between reports using the McNemar test. The satisfaction of gynecologists was compared using the marginal homogeneity test.
Structured reporting increased completeness for both Rd1 (rectosigmoid, retrocervical/uterosacral ligament, vagina, and ureter) and Rd2 (vagina, ureter, and bladder) (p < 0.05), without compromising sensitivity or specificity at any of the evaluated sites. Gynecologists\' satisfaction was superior with structured reports in most comparisons.
Structured MRI reports perform better in fully documenting essential features of PE and are similar in terms of diagnostic performance, therefore having higher potential for surgical planning. Gynecologists found them easier to assess and were more satisfied with the information provided by structured reports.

BACKGROUND: Inflammation is a necessary component of chronic kidney disease (CKD) that can be attributed to an accumulation of toxins and a reduced clearance of proinflammatory cytokines. Procalcitonin (PCT) is a widely applied biomarker in the diagnosis of infection, and considering the presence of pre-existing inflammation in CKD patients, the PCT level could be high in such a population; however, no reference value for PCT in CKD patients has been available to date.
METHODS: During the present study period, 361 CKD patients and 119 healthy controls were included. The PCT level and other biochemistry parameters were assayed by using a COBAS system. Statistical analysis was conducted to compare the differences in PCT levels and other biochemistry parameters between the two groups, and linear regression was used to assess the correlation between two variables. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the performance of PCT and the optimal cutoff value to differentiate between CKD patients and healthy controls.
RESULTS: The PCT level in CKD patients was significantly higher than that in healthy controls, and among the CKD patients, the PCT level was increased with advanced clinical stage. Moreover, PCT was moderately correlated with CysC. The optimal off-value was 0.075 with a sensitivity of 94.7% and specificity of 90.8%.
CONCLUSIONS: The PCT level was significantly higher in CKD patients than in healthy controls, and the reference value for CKD patients should be adjusted to avoid unnecessary antibiotic treatments which may pose a negative impact on residual renal function.

METHODS: 77-year-old former smoker admitted because of fatigue and abdominal distention. Past medical history positive for two previous hospitalizations for pericardial and pleural effusions (no diagnosis achieved). At admission erythrocyte sedimentation rate was 122mm per hour. Baseline investigations revealed ascitic, pleural and pericardial effusion. Effusions were tapped: neoplastic cells and acid-fast bacilli (AFB) were not identified, aerobic and mycobacterial culture resulted negative. QuantiFERON TB-Gold test was negative. Total body PET-CT and autoimmunity panel were negative. A neoplastic process was considered the most likely explanation. Before signing off the patient to comfort care, a reassessment was performed and an exposure to tuberculosis during childhood was documented. Because of constrictive pericarditis, pericardiectomy was performed: histologic examination showed chronic pericardial inflammation without granulomas, but Ziehl-Neelsen stain identified AFB and PCR was positive for Mycobacterium tuberculosis complex. Patient was started on anti-TB therapy with resolution of the effusions in the following months. Genes associated with defects in innate immunity were sequences and dentritic cells were studied, but no alterations were identified.
CONCLUSIONS: A Bayesian approach to clinical decision making should be recommended. Interpretation of diagnostic tests should take into account the imperfect diagnostic performance of the majority of these tests. Further studies to investigate genetic susceptibility to tuberculosis are needed.

Background. The lack of objective disease markers is a major cause of misdiagnosis and nonstandardized approaches in delirium. Recent studies conducted in well-selected patients and confined study environments suggest that quantitative electroencephalography (qEEG) can provide such markers. We hypothesize that qEEG helps remedy diagnostic uncertainty not only in well-defined study cohorts but also in a heterogeneous hospital population. Methods. In this retrospective case-control study, EEG power spectra of delirious patients and age-/gender-matched controls (n = 31 and n = 345, respectively) were fitted in a linear model to test their performance as binary classifiers. We subsequently evaluated the diagnostic performance of the best classifiers in control samples with normal EEGs (n = 534) and real-world samples including pathologic findings (n = 4294). Test reliability was estimated through split-half analyses. Results. We found that the combination of spectral power at F3-P4 at 2 Hz (area under the curve [AUC] = .994) and C3-O1 at 19 Hz (AUC = .993) provided a sensitivity of 100% and a specificity of 99% to identify delirious patients among normal controls. These classifiers also yielded a false positive rate as low as 5% and increased the pretest probability of being delirious by 57% in an unselected real-world sample. Split-half reliabilities were .98 and .99, respectively. Conclusion. This retrospective study yielded preliminary evidence that qEEG provides excellent diagnostic performance to identify delirious patients even outside confined study environments. It furthermore revealed reduced beta power as a novel specific finding in delirium and that a normal EEG excludes delirium. Prospective studies including parameters of pretest probability and delirium severity are required to elaborate on these promising findings.

OBJECTIVE: Accurate assessment of knee articular cartilage is clinically important. Although 3.0 Tesla (T) MRI is reported to offer improved diagnostic performance, literature regarding the clinical impact of MRI field strength is lacking. The purpose of this study is to compare the diagnostic performance of clinical MRI reports for assessment of cartilage at 1.5 and 3.0 T in comparison to arthroscopy.
METHODS: This IRB-approved retrospective study consisted of 300 consecutive knees in 297 patients who had routine clinical MRI and arthroscopy. Descriptions of cartilage from MRI reports of 165 knees at 1.5 T and 135 at 3.0 T were compared with arthroscopy. The sensitivity, specificity, percent of articular surfaces graded concordantly, and percent of articular surfaces graded within one grade of the arthroscopic grading were calculated for each articular surface at 1.5 and 3.0 T. Agreement between MRI and arthroscopy was calculated with the weighted-kappa statistic. Significance testing was performed utilizing the z-test after bootstrapping to obtain the standard error.
CONCLUSIONS: The sensitivity, specificity, percent of articular surfaces graded concordantly, and percent of articular surfaces graded within one grade were 61.4%, 82.7%, 62.2%, and 77.5% at 1.5 T and 61.8%, 80.6%, 59.5%, and 75.6% at 3.0 T, respectively. The weighted kappa statistic was 0.56 at 1.5 T and 0.55 at 3.0 T. There was no statistically significant difference in any of these parameters between 1.5 and 3.0 T. Factors potentially contributing to the lack of diagnostic advantage of 3.0 T MRI are discussed.

Major Depression Disorder (MDD) is common among mothers of young children. However, its detection remains low in primary-care and community-based settings in part due to the uncertainty regarding the validity of existing case-finding instruments. We conducted meta-analyses to estimate the diagnostic validity of commonly used maternal MDD case finding instruments in the United States.
We systematically searched three electronic bibliographic databases PubMed, PsycINFO, and EMBASE from 1994 to 2015 to identify relevant published literature. Study eligibility and quality were evaluated using the Standards for the Reporting of Diagnostic Accuracy studies and Quality Assessment of Diagnostic Accuracy Studies guidelines, respectively. Pooled sensitivity and specificity of case-finding instruments were generated using Bayesian hierarchical summary receiver operating models.
Overall, 1130 articles were retrieved and 74 articles were selected for full-text review. Twelve articles examining six maternal MDD case-finding instruments met the eligibility criteria and were included in our meta-analyses. Pooled sensitivity and specificity estimates were highest for the BDI-II (91%; 95% Bayesian Credible Interval (BCI): 68%; 99% and 89%; 95% BCI: 62%; 98% respectively) and EPDS10 (74%; 95% BCI: 46%; 91% and 97%; 95% BCI: 84%; 99% respectively) during the antepartum and postpartum periods respectively.
No meta-regression was conducted to examine the impact of study-level characteristics on the results.
Diagnostic performance varied among instruments and between peripartum periods. These findings suggest the need for a judicious selection of maternal MDD case-finding instruments depending on the study population and target periods of assessment.