Haemoparasites from clinical screening of reptiles in south-east Queensland, Australia

2004 ◽  
Vol 155 (22) ◽  
pp. 708-709 ◽  
Author(s):  
M. A. Peirce ◽  
R. D. Adlard
Keyword(s):  
Clinical Screening

Development of a Clinical Screening Tool for Undiagnosed Diabetes in a Hospital Observation Unit

Diabetes ◽  
2018 ◽  
Vol 67 (Supplement 1) ◽  
pp. 1323-P
Author(s):  
REBECCA M. NOONAN ◽  
WILFRIDO J. CASTILLO ◽  
GABRIEL M. SAFFRAN ◽  
COLLIN B. MONTGOMERY ◽  
GANESH PHAYAL ◽  
...  
Keyword(s):  
Screening Tool ◽  
Undiagnosed Diabetes ◽  
Observation Unit ◽  
Clinical Screening

scholarly journals Predicting youth diabetes risk using NHANES data and machine learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Nita Vangeepuram ◽  
Bian Liu ◽  
Po-hsiang Chiu ◽  
Linhua Wang ◽  
Gaurav Pandey
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Clinical Guideline ◽  
Diabetes Risk ◽  
Screening Tools ◽  
Clinical Screening ◽  
Predictive Values ◽  
Screening Guideline ◽  
Demographic Subgroups ◽  
Sensitivity Specificity

AbstractPrediabetes and diabetes mellitus (preDM/DM) have become alarmingly prevalent among youth in recent years. However, simple questionnaire-based screening tools to reliably assess diabetes risk are only available for adults, not youth. As a first step in developing such a tool, we used a large-scale dataset from the National Health and Nutritional Examination Survey (NHANES) to examine the performance of a published pediatric clinical screening guideline in identifying youth with preDM/DM based on American Diabetes Association diagnostic biomarkers. We assessed the agreement between the clinical guideline and biomarker criteria using established evaluation measures (sensitivity, specificity, positive/negative predictive value, F-measure for the positive/negative preDM/DM classes, and Kappa). We also compared the performance of the guideline to those of machine learning (ML) based preDM/DM classifiers derived from the NHANES dataset. Approximately 29% of the 2858 youth in our study population had preDM/DM based on biomarker criteria. The clinical guideline had a sensitivity of 43.1% and specificity of 67.6%, positive/negative predictive values of 35.2%/74.5%, positive/negative F-measures of 38.8%/70.9%, and Kappa of 0.1 (95%CI: 0.06–0.14). The performance of the guideline varied across demographic subgroups. Some ML-based classifiers performed comparably to or better than the screening guideline, especially in identifying preDM/DM youth (p = 5.23 × 10−5).We demonstrated that a recommended pediatric clinical screening guideline did not perform well in identifying preDM/DM status among youth. Additional work is needed to develop a simple yet accurate screener for youth diabetes risk, potentially by using advanced ML methods and a wider range of clinical and behavioral health data.

Positive predictive values in clinical screening for developmental dysplasia of the hip

2021 ◽  
Author(s):  
Hans‐Christen Husum ◽  
Arash Gaffari ◽  
Laura Amalie Rytoft ◽  
Jens Svendsson ◽  
Søren Harving ◽  
...  
Keyword(s):  
Developmental Dysplasia ◽  
Clinical Screening ◽  
Predictive Values ◽  
Dysplasia Of The Hip

scholarly journals Validation of a French version of the Sleep Condition Indicator: a clinical screening tool for insomnia disorder according to DSM-5 criteria

2017 ◽  
Vol 26 (6) ◽  
pp. 702-708 ◽  
Author(s):  
Sophie Bayard ◽  
Cindy Lebrun ◽  
Khaalid Hassan Maudarbocus ◽  
Vanessa Schellaert ◽  
Alicia Joffre ◽  
...  
Keyword(s):  
Screening Tool ◽  
Clinical Screening ◽  
French Version ◽  
Dsm 5 ◽  
Insomnia Disorder ◽  
Sleep Condition ◽  
Condition Indicator

Psychometric Testing and Refinement of the Support Needs Inventory for Parents of Asthmatic Children

2004 ◽  
Vol 12 (3) ◽  
pp. 179-193
Author(s):  
Christine Toye ◽  
Linda J. Kristjanson ◽  
Mardhie E. Coleman ◽  
Hendrika Maltby ◽  
Glenda Jackson
Keyword(s):  
Factor Analysis ◽  
Internal Consistency ◽  
Psychometric Testing ◽  
Internal Consistency Reliability ◽  
Support Needs ◽  
Clinical Screening ◽  
Asthmatic Children ◽  
Children With Asthma ◽  
Undue Burden ◽  
Over Time

This study refined the Support Needs Inventory for Parents of Asthmatic Children (SNIPAC) (Coleman, Maltby, Kristjanson, & Robinson, 2001) to produce a more parsimonious tool to assess the importance and meet the support needs of parents of children with asthma. The original tool was completed by 145 parents of 199 children with asthma, and 74 of these also provided test-retest responses. Internal consistency reliability, construct validity, and stability over time were assessed and refinements were made. Internal consistency reliability of the revised 20-item tool ranged from .77 to .95 for the three subscales of the Parent’s Priority Scale (PPS), and .92 for the full PPS. Cronbach’s alphas ranged from .74 to .90 for the three subscales of the Parent’s Fulfillment Scale (PFS) and was .91 for the full scale. Factor analysis results of the PPS were compatible with the tool’s conceptual framework. The revised 20-item tool demonstrated adequate psychometric properties in most areas. This tool may be used for research or clinical screening without imposing undue burden on parents. Further work is required to establish the tool’s stability over time.

Abstract 2928: Sudden Arrhythmic Death Syndrome-Diagnostic Yield Of Combined Clinical & Genetic Screening In A British Specialist Clinic

Circulation ◽  
2007 ◽  
Vol 116 (suppl_16) ◽  
Author(s):  
Pier D Lambiase ◽  
Juan C Kaski ◽  
Eileen Firman ◽  
Perry M Elliott ◽  
Akbar K Ahmed ◽  
...  
Keyword(s):  
Heart Disease ◽  
Genetic Testing ◽  
Screening Programme ◽  
Diagnostic Yield ◽  
Challenge Test ◽  
Structural Heart Disease ◽  
Clinical Screening ◽  
Arrhythmic Death ◽  
Sudden Arrhythmic Death Syndrome ◽  
Index Cases

Introduction: Sudden arrhythmic death syndrome (SADS) arises through disorders of ion channel function or structural heart disease. It accounts for over 400 deaths in the UK per annum. To date there has been no comprehensive analysis of the diagnostic yield and efficacy of a family screening approach in SADS index cases where the post mortem heart is structurally normal after expert pathological review. Methods: 118 SADS families where the SADS victim died between 1 and 35 years of age were evaluated in a systematic family screening programme between 2003–2006. All SADS index cases had a structurally normal heart after expert review of all available tissue. All studied relatives underwent resting, signal averaged ECG, 24h Holter, exercise ECG with V0 2 max, transthoracic echocardiography and an ajmaline challenge test after initial clinical screening. Systematic mutation analysis was performed on the known long QT (LQT)genes including SCN5A & ryanodine receptor/ARVC genes when clinically suspected. Results: The most common modes of death were rest in 28%, sleep in 25% and exercise in 18%. Clinical screening identified an inherited electrical cause of SADS in 41 of the 118 families (35%)-20 Brugada, 18 LQT Syndrome, 3 Catecholiminergic Polymorphic Ventricular Tachycardia (CPVT). Structural heart disease was identified in 5 ARVC & 2 DCM families. 26 ICDs have been implanted in affected family members-4 LQTS, 7 Brugada, 2 CPVT, 2 ARVC, 2 DCM and 9 on clinical grounds without a definitive diagnosis. The ECG (37%) and ajmaline challenge test (49%) had the highest diagnostic yield in families with a positive diagnosis. To date, genetic testing has increased the diagnostic yield by 5% (6/118 families-2 KCNQ1, 1 HERG, 2 SCN5A, 1 ARVC ), confirming a clinical diagnosis in 6.6%–3 KCNQ1, 3 SCN5A, 1 HERG, 1 KCNH2. Conclusions: Systematic clinical screening in relatives of SADS victims has a diagnostic yield of 35% increasing to 40% with genetic testing. Electrical causes of SADS predominate in these families. These findings demonstrate that a systematic clinical screening programme in SADS families is both achievable and effective. The full impact of gene testing (including RyR mutations) upon diagnostic yield is awaited.

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