Earwax: A neglected body secretion or a step ahead in clinical diagnosis? A pilot study

2017 ◽  
Vol 159 ◽  
pp. 92-101 ◽  
Author(s):  
Engy Shokry ◽  
Anselmo Elcana de Oliveira ◽  
Melissa Ameloti Gomes Avelino ◽  
Mariana Moreira de Deus ◽  
Nelson Roberto Antoniosi Filho
Keyword(s):  
Pilot Study ◽  
Clinical Diagnosis ◽  
Body Secretion

Electroencephalography recording analysis in monitoring of canine idiopathic epilepsy treated with phenobarbital. Pilot study

2014 ◽  
Vol 17 (4) ◽  
pp. 717-719 ◽  
Author(s):  
A. Bocheńska ◽  
M. Kwiatkowska ◽  
A. Pomianowski ◽  
T. Monowid ◽  
Z. Adamiak
Keyword(s):  
Veterinary Medicine ◽  
Retrospective Study ◽  
Pilot Study ◽  
Clinical Diagnosis ◽  
Diagnostic Tool ◽  
Seizure Activity ◽  
Idiopathic Epilepsy ◽  
Delta Band ◽  
Before And After ◽  
Recording Analysis

Abstract EEG recording is used in veterinary medicine as a diagnostic tool to support clinical diagnosis of idiopathic epilepsy and to identify focal seizure activity. This retrospective study was designed to compare EEG procedures in 23 dogs with idiopathic epilepsy before and after phenobarbital treatment. Differences among standard deviations for particular bands were significant. During phenobarbital treatment the delta band decreased.

scholarly journals Assisting differential clinical diagnosis of cattle diseases using smartphone-based technology in low resource settings: a pilot study

2017 ◽  
Vol 13 (1) ◽  
Author(s):  
Tariku Jibat Beyene ◽  
Amanuel Eshetu ◽  
Amina Abdu ◽  
Etenesh Wondimu ◽  
Ashenafi Feyisa Beyi ◽  
...  
Keyword(s):  
Pilot Study ◽  
Clinical Diagnosis ◽  
Low Resource Settings ◽  
Low Resource ◽  
Cattle Diseases

scholarly journals Inter-Rater Agreement in the Clinical Diagnosis of Essential Tremor: Data from the NEDICES-2 Pilot Study

2014 ◽  
Vol 4 (0) ◽  
pp. 04
Author(s):  
Fernando Sierra-Hidalgo ◽  
Juan Pablo Romero ◽  
Felix Bermejo-Pareja ◽  
Alvaro Sánchez-Ferro ◽  
Jesús Hernández-Gallego ◽  
...  
Keyword(s):  
Pilot Study ◽  
Essential Tremor ◽  
Clinical Diagnosis ◽  
Rater Agreement

scholarly journals Pilot study on nocturnal monitoring of crackles in children with pneumonia

2021 ◽  
pp. 00284-2021
Author(s):  
Wilfried Nikolaizik ◽  
Lisa Wuensch ◽  
Monika Bauck ◽  
Volker Gross ◽  
Keywan Sohrabi ◽  
...  
Keyword(s):  
Pilot Study ◽  
Clinical Diagnosis ◽  
Monitor System ◽  
Lung Sound ◽  
Follow Up Study ◽  
Acute Pneumonia ◽  
Time Periods ◽  
Characteristic Features ◽  
Lung Sound Monitoring

BackgroundThe clinical diagnosis of pneumonia is usually based on crackles at auscultation but it is not yet clear what kind of crackles are the characteristic features of pneumonia in children. Lung sound monitoring can be used as a “longtime stethoscope”. Therefore, it was the aim of this pilot study to use a lung sound monitor system to detect crackles and to differentiate between fine and coarse crackles in children with acute pneumonia. The change of crackles during the course of the disease shall be investigated in a follow-up study.Patients and methodsCrackles were recorded overnight from 22.00 to 06.00 h in 30 children with radiographically confirmed pneumonia. The data of a total of 28 800 recorded 30-second-epochs were audiovisually analysed for fine and coarse crackles.ResultsFine crackles and coarse crackles were recognised in every patient with pneumonia but the number of epochs with and without crackles varied widely among the different patients: Fine crackles were detected in 40% (mean, sd 22), coarse crackles in 76% (sd 20). The predominant localisation of crackles as recorded during overnight monitoring was in accordance with the radiographic infiltrates and the classical auscultation in most patients. The distribution of crackles was fairly equal throughout the night. However, there were time periods without any crackle in the single patients so that the diagnosis of pneumonia might be missed at sporadic auscultation.ConclusionNocturnal monitoring can be beneficial to reliably detect fine and coarse crackles in children with pneumonia.

scholarly journals Testing the accuracy of an observation-based classifier for rapid detection of autism risk

2014 ◽  
Vol 4 (8) ◽  
pp. e424-e424 ◽  
Author(s):  
M Duda ◽  
J A Kosmicki ◽  
D P Wall
Keyword(s):  
Pilot Study ◽  
Clinical Diagnosis ◽  
Autism Diagnostic Observation Schedule ◽  
High Accuracy ◽  
Small Sample ◽  
Diagnostic Validity ◽  
Statistical Accuracy ◽  
Data Repositories ◽  
Best Estimate ◽  
Autism Diagnostic Observation

Abstract Current approaches for diagnosing autism have high diagnostic validity but are time consuming and can contribute to delays in arriving at an official diagnosis. In a pilot study, we used machine learning to derive a classifier that represented a 72% reduction in length from the gold-standard Autism Diagnostic Observation Schedule-Generic (ADOS-G), while retaining >97% statistical accuracy. The pilot study focused on a relatively small sample of children with and without autism. The present study sought to further test the accuracy of the classifier (termed the observation-based classifier (OBC)) on an independent sample of 2616 children scored using ADOS from five data repositories and including both spectrum (n=2333) and non-spectrum (n=283) individuals. We tested OBC outcomes against the outcomes provided by the original and current ADOS algorithms, the best estimate clinical diagnosis, and the comparison score severity metric associated with ADOS-2. The OBC was significantly correlated with the ADOS-G (r=−0.814) and ADOS-2 (r=−0.779) and exhibited >97% sensitivity and >77% specificity in comparison to both ADOS algorithm scores. The correspondence to the best estimate clinical diagnosis was also high (accuracy=96.8%), with sensitivity of 97.1% and specificity of 83.3%. The correlation between the OBC score and the comparison score was significant (r=−0.628), suggesting that the OBC provides both a classification as well as a measure of severity of the phenotype. These results further demonstrate the accuracy of the OBC and suggest that reductions in the process of detecting and monitoring autism are possible.

Sign in / Sign up

Export Citation Format

Share Document