Video-Polysomnographic Assessment for the Diagnosis of Disorders of Arousal in Children

Neurology ◽  
2020 ◽  
Vol 96 (1) ◽  
pp. e121-e130
Author(s):  
Régis Lopez ◽  
Christine Laganière ◽  
Sofiène Chenini ◽  
Anna Laura Rassu ◽  
Elisa Evangelista ◽  
...  
Keyword(s):  
Operating Characteristic ◽  
Slow Wave Sleep ◽  
Area Under The Curve ◽  
Class Iii ◽  
Receiver Operating Characteristic Curves ◽  
Arousal Index ◽  
Fragmentation Index ◽  
Increased Sensitivity ◽  
Disorders Of Arousal

ObjectivesTo highlight the slow-wave sleep (SWS) fragmentation and validate the video-polysomnographic (vPSG) criteria and cutoffs for the diagnosis of disorders of arousal (DOA) in children, as already reported in adults.MethodsOne hundred children (66 boys, 11.0 ± 3.3 years) with frequent episodes of DOA and 50 nonparasomniac children (32 boys, 10.9 ± 3.9 years) underwent vPSG recording to quantify SWS characteristics (number of N3 sleep interruptions, fragmentation index, slow/mixed and fast arousal ratios, and indexes per hour) and associated behaviors. We compared SWS characteristics in the 2 groups and defined the optimal cutoff values for the diagnosis of DOA using receiver operating characteristic curves.ResultsPatients with DOA had higher amounts of N3 and REM sleep, number of N3 interruptions, SWS fragmentation, and slow/mixed arousal indexes than controls. The highest area under the curve (AUC) values were obtained for SWS fragmentation and slow/mixed arousal indexes with satisfactory classification performances (AUC 0.80, 95% confidence interval [CI] 0.73–0.87; AUC 0.82, 95% CI 0.75–0.89). SWS fragmentation index cutoff value of 4.1/h reached a sensitivity of 65.0% and a specificity of 84.0%. Slow/mixed arousal index cutoff of 3.8/h reached a sensitivity of 69.0% and a specificity of 82.0%. At least one parasomniac episode was recorded in 63.0% of patients and none of the controls. Combining behavioral component by vPSG increased sensitivity of both biomarkers to 83% and 89%, respectively.ConclusionsWe confirmed that SWS fragmentation and slow/mixed arousal indexes are 2 relevant biomarkers for the diagnosis of DOA in children, with different cutoffs obtained than those validated in adults.Classification of EvidenceThis study provides Class III evidence that SWS fragmentation and slow/mixed arousal indexes on vPSG accurately identify children with DOA.

scholarly journals Evaluation of alpha-l-fucosidase for the diagnosis of hepatocellular carcinoma based on meta-analysis

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Lei Xi ◽  
Chunqing Yang
Keyword(s):  
Hepatocellular Carcinoma ◽  
Operating Characteristic ◽  
Meta Analysis ◽  
Area Under The Curve ◽  
Diagnostic Odds Ratio ◽  
Diagnostic Value ◽  
Receiver Operating Characteristic Curves ◽  
Sensitivity Specificity ◽  
Review Manager

AbstractObjectivesThe main aim of the present study was to assess the diagnostic value of alpha-l-fucosidase (AFU) for hepatocellular carcinoma (HCC).MethodsStudies that explored the diagnostic value of AFU in HCC were searched in EMBASE, SCI, and PUBMED. The sensitivity, specificity, and DOR about the accuracy of serum AFU in the diagnosis of HCC were pooled. The methodological quality of each article was evaluated with QUADAS-2 (quality assessment for studies of diagnostic accuracy 2). Receiver operating characteristic curves (ROC) analysis was performed. Statistical analysis was conducted by using Review Manager 5 and Open Meta-analyst.ResultsEighteen studies were selected in this study. The pooled estimates for AFU vs. α-fetoprotein (AFP) in the diagnosis of HCC in 18 studies were as follows: sensitivity of 0.7352 (0.6827, 0.7818) vs. 0.7501 (0.6725, 0.8144), and specificity of 0.7681 (0.6946, 0.8283) vs. 0.8208 (0.7586, 0.8697), diagnostic odds ratio (DOR) of 7.974(5.302, 11.993) vs. 13.401 (8.359, 21.483), area under the curve (AUC) of 0.7968 vs. 0.8451, respectively.ConclusionsAFU is comparable to AFP for the diagnosis of HCC.

scholarly journals Usefulness of sialic acid for diagnosis of sepsis in critically ill patients: a retrospective study

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Bo Yao ◽  
Wen-juan Liu ◽  
Di Liu ◽  
Jin-yan Xing ◽  
Li-juan Zhang
Keyword(s):  
Sialic Acid ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Incomplete Data ◽  
Acid Level ◽  
Operating Characteristic ◽  
Area Under The Curve ◽  
Diagnostic Efficacy ◽  
Receiver Operating Characteristic Curves ◽  
Sensitivity Specificity

Abstract Background Early diagnosis of sepsis is very important. It is necessary to find effective and adequate biomarkers in order to diagnose sepsis. In this study, we compared the value of sialic acid and procalcitonin for diagnosing sepsis. Methods Newly admitted intensive care unit patients were enrolled from January 2019 to June 2019. We retrospectively collected patient data, including presence of sepsis or not, procalcitonin level and sialic acid level. Receiver operating characteristic curves for the ability of sialic acid, procalcitonin and combination of sialic acid and procalcitonin to diagnose sepsis were carried out. Results A total of 644 patients were admitted to our department from January 2019 to June 2019. The incomplete data were found in 147 patients. Finally, 497 patients data were analyzed. The sensitivity, specificity and area under the curve for the diagnosis of sepsis with sialic acid, procalcitonin and combination of sialic acid and procalcitonin were 64.2, 78.3%, 0.763; 67.9, 84.0%, 0.816 and 75.2, 84.6%, 0.854. Moreover, sialic acid had good values for diagnosing septic patients with viral infection, with 87.5% sensitivity, 82.2% specificity, and 0.882 the area under the curve. Conclusions Compared to procalcitonin, sialic acid had a lower diagnostic efficacy for diagnosing sepsis in critically ill patients. However, the combination of sialic acid and procalcitonin had a higher diagnostic efficacy for sepsis. Moreover, sialic acid had good value for diagnosing virus-induced sepsis.

Considerations on the region of interest in the ROC space

2021 ◽  
pp. 096228022110605
Author(s):  
Luigi Lavazza ◽  
Sandro Morasca
Keyword(s):  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
False Positive Rate ◽  
Area Under The Curve ◽  
Region Of Interest ◽  
True Positive Rate ◽  
True Positive ◽  
Receiver Operating Characteristic Curves ◽  
Positive Rate ◽  
Receiver Operating

Receiver Operating Characteristic curves have been widely used to represent the performance of diagnostic tests. The corresponding area under the curve, widely used to evaluate their performance quantitatively, has been criticized in several respects. Several proposals have been introduced to improve area under the curve by taking into account only specific regions of the Receiver Operating Characteristic space, that is, the plane to which Receiver Operating Characteristic curves belong. For instance, a region of interest can be delimited by setting specific thresholds for the true positive rate or the false positive rate. Different ways of setting the borders of the region of interest may result in completely different, even opposing, evaluations. In this paper, we present a method to define a region of interest in a rigorous and objective way, and compute a partial area under the curve that can be used to evaluate the performance of diagnostic tests. The method was originally conceived in the Software Engineering domain to evaluate the performance of methods that estimate the defectiveness of software modules. We compare this method with previous proposals. Our method allows the definition of regions of interest by setting acceptability thresholds on any kind of performance metric, and not just false positive rate and true positive rate: for instance, the region of interest can be determined by imposing that [Formula: see text] (also known as the Matthews Correlation Coefficient) is above a given threshold. We also show how to delimit the region of interest corresponding to acceptable costs, whenever the individual cost of false positives and false negatives is known. Finally, we demonstrate the effectiveness of the method by applying it to the Wisconsin Breast Cancer Data. We provide Python and R packages supporting the presented method.

scholarly journals Performance of the Kidney Failure Risk Equation by Disease Etiology in Advanced CKD

2020 ◽  
Vol 15 (10) ◽  
pp. 1424-1432
Author(s):  
Gregory L. Hundemer ◽  
Navdeep Tangri ◽  
Manish M. Sood ◽  
Tim Ramsay ◽  
Ann Bugeja ◽  
...  
Keyword(s):  
Kidney Disease ◽  
Confidence Interval ◽  
Kidney Failure ◽  
Operating Characteristic ◽  
Area Under The Curve ◽  
Polycystic Kidney ◽  
Receiver Operating Characteristic Curves ◽  
Time Points ◽  
Risk Equation ◽  
Failure Risk

Background and objectivesThe kidney failure risk equation is a clinical tool commonly used for prediction of progression from CKD to kidney failure. The kidney failure risk equation’s accuracy in advanced CKD and whether this varies by CKD etiology remains unknown. This study examined the kidney failure risk equation’s discrimination and calibration at 2 and 5 years among a large tertiary care population with advanced CKD from heterogeneous etiologies.Design, setting, participants, & measurementsThis retrospective cohort study included 1293 patients with advanced CKD (median eGFR 15 ml/min per 1.73 m2) referred to the Ottawa Hospital Multi-Care Kidney Clinic between 2010 and 2016, with follow-up clinical data available through 2018. Four-variable kidney failure risk equation scores for 2- and 5-year risks of progression to kidney failure (defined as dialysis or kidney transplantation) were calculated upon initial referral and correlated with the subsequent observed kidney failure incidence within these time frames. Receiver operating characteristic curves and calibration plots were used to measure the discrimination and calibration of the kidney failure risk equation both in the overall advanced CKD population and by CKD etiology: diabetic kidney disease, hypertensive nephrosclerosis, GN, polycystic kidney disease, and other. Pairwise comparisons of the receiver operating characteristic curves by CKD etiology were performed to compare kidney failure risk equation discrimination.ResultsThe kidney failure risk equation provided adequate to excellent discrimination in identifying patients with CKD likely to progress to kidney failure at the 2- and 5-year time points both overall (2-year area under the curve, 0.83; 95% confidence interval, 0.81 to 0.85; 5-year area under the curve, 0.81; 95% confidence interval, 0.77 to 0.84) and across CKD etiologies. The kidney failure risk equation displayed adequate calibration at the 2- and 5-year time points both overall and across CKD etiologies (Hosmer–Lemeshow P≥0.05); however, the predicted risks of kidney failure were higher than the observed risks across CKD etiologies with the exception of polycystic kidney disease.ConclusionsThe kidney failure risk equation provides adequate discrimination and calibration in advanced CKD and across CKD etiologies.

scholarly journals REM sleep atonia loss distinguishes synucleinopathy in older adults with cognitive impairment

Neurology ◽  
2019 ◽  
Vol 94 (1) ◽  
pp. e15-e29 ◽  
Author(s):  
Stuart J. McCarter ◽  
Grace M. Tabatabai ◽  
Ho-Yann Jong ◽  
David J. Sandness ◽  
Paul C. Timm ◽  
...  
Keyword(s):  
Older Adults ◽  
Cognitive Impairment ◽  
Rem Sleep ◽  
Area Under The Curve ◽  
Class Iii ◽  
Receiver Operating Characteristic Curves ◽  
Diagnostic Thresholds ◽  
Rem Atonia ◽  
Clinical Tools ◽  
Anterior Tibialis

ObjectiveTo determine whether quantitative polysomnographic REM sleep without atonia (RSWA) distinguishes between cognitive impairment phenotypes.BackgroundNeurodegenerative cognitive impairment in older adults predominantly correlates with tauopathy or synucleinopathy. Accurate antemortem phenotypic diagnosis has important prognostic and treatment implications; additional clinical tools might distinguish between dementia syndromes.MethodsWe quantitatively analyzed RSWA in 61 older adults who underwent polysomnography including 46 with cognitive impairment (20 probable synucleinopathy), 26 probable non-synucleinopathy (15 probable Alzheimer disease, 11 frontotemporal lobar dementia), and 15 age- and sex-matched controls. Submentalis and anterior tibialis RSWA metrics and automated REM atonia index were calculated. Group statistical comparisons and regression were performed, and receiver operating characteristic curves determined diagnostic RSWA thresholds that best distinguished synucleinopathy phenotype.ResultsSubmentalis—but not anterior tibialis RSWA—was greater in synucleinopathy than nonsynucleinopathy; several RSWA diagnostic thresholds distinguished synucleinopathy with excellent specificity including submentalis tonic, 5.6% (area under the curve [AUC] 0.791); submentalis any, 15.0% (AUC 0.871); submentalis phasic, 10.8% (AUC 0.863); and anterior tibialis phasic, 31.4% (AUC 0.694). In the subset of patients without dream enactment behaviors, submentalis RSWA was also greater in patients with synucleinopathy than in those without synucleinopathy. RSWA was detected more frequently by quantitative than qualitative methods (p = 0.0001).ConclusionElevated submentalis RSWA distinguishes probable synucleinopathy from probable nonsynucleinopathy in cognitively impaired older adults, even in the absence of clinical dream enactment symptoms.Classification of evidenceThis study provides Class III evidence that quantitative RSWA analysis is useful for distinguishing cognitive impairment phenotypes. Further studies with pathologic confirmation of dementia diagnoses are needed to confirm the diagnostic utility of RSWA in dementia.

Prediction of regaining consciousness despite an early epileptiform EEG after cardiac arrest

Neurology ◽  
2020 ◽  
Vol 94 (16) ◽  
pp. e1675-e1683 ◽  
Author(s):  
Giuseppina Barbella ◽  
Jong Woo Lee ◽  
Vincent Alvarez ◽  
Jan Novy ◽  
Mauro Oddo ◽  
...  
Keyword(s):  
Cardiac Arrest ◽  
Operating Characteristic ◽  
Neuronal Damage ◽  
Cerebral Performance Category ◽  
Neuron Specific Enolase ◽  
Area Under The Curve ◽  
Performance Category ◽  
Receiver Operating Characteristic Curves ◽  
Normal Background ◽  
Serum Neuron Specific Enolase

ObjectiveAfter cardiac arrest (CA), epileptiform EEG, occurring in about 1/3 of patients, often but not invariably heralds poor prognosis. We tested the hypothesis that a combination of specific EEG features identifies patients who may regain consciousness despite early epileptiform patterns.MethodsWe retrospectively analyzed a registry of comatose patients post-CA (2 Swiss centers), including those with epileptiform EEG. Background and epileptiform features in EEGs 12–36 hours or 36–72 hours from CA were scored according to the American Clinical Neurophysiology Society nomenclature. Best Cerebral Performance Category (CPC) score within 3 months (CPC 1–3 vs 4–5) was the primary outcome. Significant EEG variables were combined in a score assessed with receiver operating characteristic curves, and independently validated in a US cohort; its correlation with serum neuron-specific enolase (NSE) was also tested.ResultsOf 488 patients, 107 (21.9%) had epileptiform EEG <72 hours; 18 (17%) reached CPC 1–3. EEG 12–36 hours background continuity ≥50%, absence of epileptiform abnormalities (p < 0.00001 each), 12–36 and 36–72 hours reactivity (p < 0.0001 each), 36–72 hours normal background amplitude (p = 0.0004), and stimulus-induced discharges (p = 0.0001) correlated with favorable outcome. The combined 6-point score cutoff ≥2 was 100% sensitive (95% confidence interval [CI], 78%–100%) and 70% specific (95% CI, 59%–80%) for CPC 1–3 (area under the curve [AUC], 0.98; 95% CI, 0.94–1.00). Increasing score correlated with NSE (ρ = −0.46, p = 0.0001). In the validation cohort (41 patients), the score was 100% sensitive (95% CI, 60%–100%) and 88% specific (95% CI, 73%–97%) for CPC 1–3 (AUC, 0.96; 95% CI, 0.91–1.00).ConclusionPrognostic value of early epileptiform EEG after CA can be estimated combining timing, continuity, reactivity, and amplitude features in a score that correlates with neuronal damage.

scholarly journals Assessment Tools for Identifying Functional Limitations Associated With Functional Ankle Instability

2008 ◽  
Vol 43 (1) ◽  
pp. 44-50 ◽  
Author(s):  
Scott E. Ross ◽  
Kevin M. Guskiewicz ◽  
Michael T. Gross ◽  
Bing Yu
Keyword(s):  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Assessment Tool ◽  
Ankle Instability ◽  
Functional Limitations ◽  
Area Under The Curve ◽  
Assessment Tools ◽  
Functional Ankle Instability ◽  
Receiver Operating Characteristic Curves ◽  
Time To Stabilization

Abstract Context: Assessment tools should identify functional limitations associated with functional ankle instability (FAI) by discriminating unstable from stable ankles. Objective: To identify assessment tools that discriminated FAI from stable ankles and determine the most accurate assessment tool for discriminating between FAI and stable ankles. Design: Case-control study. Setting: Research laboratory. Patients or Other Participants: Fifteen individuals with FAI and 15 healthy individuals; participants with unilateral FAI reported “giving-way” sensations and ankle sprains, whereas healthy participants did not. Intervention(s): Participants answered 12 questions on the Ankle Joint Functional Assessment Tool (AJFAT). They also performed a single-leg jump landing, which required them to jump to half their maximum jump height, land on a single leg, and stabilize quickly on a force plate. Main Outcome Measure(s): Receiver operating characteristic curves determined cutoff scores for discriminating between ankle groups for AJFAT total score and resultant vector (RV) time to stabilization. Accuracy values for discriminating between groups were determined by calculating the area under the receiver operating characteristic curves. Results: The cutoff score for discriminating between FAI and stable ankles was ≥26 (sensitivity  =  1, specificity  =  1) and ≥1.58 seconds (sensitivity  =  0.67, specificity  =  0.73) for the AJFAT total score and RV time to stabilization, respectively. The area under the curve for the AJFAT was 1.0 (asymptotic significance &lt;.05), whereas the RV time to stabilization had an area under the curve of 0.72 (asymptotic significance &lt;.05). Conclusions: The AJFAT was an excellent assessment tool for discriminating between ankle groups, whereas RV time to stabilization was a fair assessment tool. Although both assessments discriminated between ankle groups, the AJFAT more accurately discriminated between groups than the RV time to stabilization did. Future researchers should confirm these findings using a prospective research design.

scholarly journals A semi-automated measurement technique for the assessment of radiolucency

2014 ◽  
Vol 11 (96) ◽  
pp. 20140303 ◽  
Author(s):  
E. C. Pegg ◽  
B. J. L. Kendrick ◽  
H. G. Pandit ◽  
H. S. Gill ◽  
D. W. Murray
Keyword(s):  
Quantitative Assessment ◽  
Operating Characteristic ◽  
Area Under The Curve ◽  
Imaging Algorithm ◽  
Receiver Operating Characteristic Curves ◽  
Automated Method ◽  
Automated Imaging ◽  
Kappa Value ◽  
Patients At Risk ◽  
Total Agreement

The assessment of radiolucency around an implant is qualitative, poorly defined and has low agreement between clinicians. Accurate and repeatable assessment of radiolucency is essential to prevent misdiagnosis, minimize cases of unnecessary revision, and to correctly monitor and treat patients at risk of loosening and implant failure. The purpose of this study was to examine whether a semi-automated imaging algorithm could improve repeatability and enable quantitative assessment of radiolucency. Six surgeons assessed 38 radiographs of knees after unicompartmental knee arthroplasty for radiolucency, and results were compared with assessments made by the semi-automated program. Large variation was found between the surgeon results, with total agreement in only 9.4% of zones and a kappa value of 0.602; whereas the automated program had total agreement in 81.6% of zones and a kappa value of 0.802. The software had a ‘fair to excellent’ prediction of the presence or the absence of radiolucency, where the area under the curve of the receiver operating characteristic curves was 0.82 on average. The software predicted radiolucency equally well for cemented and cementless implants ( p = 0.996). The identification of radiolucency using an automated method is feasible and these results indicate that it could aid the definition and quantification of radiolucency.

scholarly journals Salivary IL-8 as a putative predictive biomarker of radiotherapy response in head and neck cancer patients

2021 ◽  
Author(s):  
Sara Principe ◽  
Enrique Zapater-Latorre ◽  
Leo Arribas ◽  
Enrique Garcia-Miragall ◽  
Jose Bagan
Keyword(s):  
Operating Characteristic ◽  
Area Under The Curve ◽  
Predictive Biomarker ◽  
Good Clinical Response ◽  
Receiver Operating Characteristic Curves ◽  
Irradiation Treatment ◽  
Tnf Α ◽  
Irradiation Therapy ◽  
Before And After ◽  
Radiotherapy Treatment

Abstract Objectives Ionizing radiation increases the expression of a number of salivary proteins involved in immunoregulatory networks related to infection, injury, inflammation, and cancer. Our main objective was to analyze whether there are significant differences in salivary cytokines before and after radiotherapy and whether any of them are associated to better outcomes after radiotherapy serving as a potential predictive biomarker of response to the treatment. Materials and methods We analyzed a panel of eight salivary markers (IL-4, IL-6, IL-8, and IL-10; MCP-1; TNF-α; VEGF; and EGF) in a group of HNC patients (N = 30), before and after irradiation treatment pre- and post-RT. We also compared these results with a group of healthy controls (N = 37). In both groups, we used stimulated saliva and we performed immunoassays based on multi-analyte profiling technology (Luminex xMAP). Results In our group of 30 HNC patients, 24 of them showed a good clinical response after radiotherapy treatment while 6 cases did not respond to radiotherapy. The data revealed a post-treatment increase in multiple cytokines in the stimulated saliva of HNC patients; the increases in IL-8 and MCP-1 were statistically significant (p ≤ 0.001 and p ≤ 0.0001, respectively). Analysis of receiver operating characteristic curves indicated the strong potential of IL-8 as a predictive biomarker of RT good outcomes (area under the curve = 0.84; p = 0.018). Conclusions After analyzing the panel of salivary cytokines, IL-8 showed the best association to the response to radiotherapy; in this sense, low IL-8 levels in the saliva of HNC patients before receiving irradiation therapy are associated with positive RT outcomes. Clinical relevance Salivary IL-8 expression in HNC patients undergoing RT may serve as a potential predictive biomarker of response to the treatment.

Tumor volume improves preoperative differentiation of prolactinomas and nonfunctioning pituitary adenomas

Endocrine ◽  
2021 ◽  
Author(s):  
Kyla Wright ◽  
Matthew Lee ◽  
Natalie Escobar ◽  
Donato Pacione ◽  
Matthew Young ◽  
...  
Keyword(s):  
Operating Characteristic ◽  
Tumor Volume ◽  
Area Under The Curve ◽  
Binary Logistic Regression ◽  
Serum Prolactin ◽  
Imaging Features ◽  
Effective Management ◽  
Receiver Operating Characteristic Curves ◽  
T2 Hyperintensity ◽  
Current Guidelines

Abstract Purpose Both prolactinomas and nonfunctioning adenomas (NFAs) can present with hyperprolactinemia. Distinguishing them is critical because prolactinomas are effectively managed with dopamine agonists, whereas compressive NFAs are treated surgically. Current guidelines rely only on serum prolactin (PRL) levels, which are neither sensitive nor specific enough. Recent studies suggest that accounting for tumor volume may improve diagnosis. The objective of this study is to investigate the diagnostic utility of PRL, tumor volume, and imaging features in differentiating prolactinoma and NFA. Methods Adult patients with pathologically confirmed prolactinoma (n = 21) or NFA with hyperprolactinemia (n = 58) between 2013 and 2020 were retrospectively identified. Diagnostic performance of clinical and imaging variables was analyzed using receiver-operating characteristic curves to calculate area under the curve (AUC). Results Tumor volume and PRL positively correlated for prolactinoma (r = 0.4839, p = 0.0263) but not for NFA (r = 0.0421, p = 0.7536). PRL distinguished prolactinomas from NFAs with an AUC of 0.8892 (p < 0.0001) and optimal cut-off value of 62.45 ng/ml, yielding a sensitivity of 85.71% and specificity of 94.83%. The ratio of PRL to tumor volume had an AUC of 0.9647 (p < 0.0001) and optimal cut-off value of 21.62 (ng/ml)/cm3 with sensitivity of 100% and specificity of 82.76%. Binary logistic regression found that PRL was a significant positive predictor of prolactinoma diagnosis, whereas tumor volume, presence of CSI not previously defined, and T2 hyperintensity were significant negative predictors. The regression model had an AUC of 0.9915 (p < 0.0001). Conclusions Consideration of tumor volume improves differentiation between prolactinomas and NFAs, which in turn leads to effective management.

Sign in / Sign up

Export Citation Format

Share Document