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Common Audiological Functional Parameters (CAFPAs): statistical and compact representation of rehabilitative audiological classification based on expert knowledge.
- Source :
-
International journal of audiology [Int J Audiol] 2019 Apr; Vol. 58 (4), pp. 231-245. - Publication Year :
- 2019
-
Abstract
- Objective: As a step towards objectifying audiological rehabilitation and providing comparability between different test batteries and clinics, the Common Audiological Functional Parameters (CAFPAs) were introduced as a common and abstract representation of audiological knowledge obtained from diagnostic tests.<br />Design: Relationships between CAFPAs as an intermediate representation between diagnostic tests and audiological findings, diagnoses and treatment recommendations (summarised as "diagnostic cases") were established by means of an expert survey. Expert knowledge was collected for 14 given categories covering different diagnostic cases. For each case, the experts were asked to indicate expected ranges of diagnostic test outcomes, as well as traffic light-encoded CAFPAs.<br />Study Sample: Eleven German experts in the field of audiological rehabilitation from Hanover and Oldenburg participated in the survey.<br />Results: Audiological findings or treatment recommendations could be distinguished by a statistical model derived from the experts' answers for CAFPAs as well as audiological tests.<br />Conclusions: The CAFPAs serve as an abstract, comprehensive representation of audiological knowledge. If more detailed information on certain functional aspects of the auditory system is required, the CAFPAs indicate which information is missing. The statistical graphical representations for CAFPAs and audiological tests are suitable for audiological teaching material; they are universally applicable for real clinical databases.
- Subjects :
- Data Interpretation, Statistical
Hearing Disorders classification
Hearing Disorders therapy
Humans
Predictive Value of Tests
Probability
Reproducibility of Results
Audiology statistics & numerical data
Correction of Hearing Impairment statistics & numerical data
Expert Systems
Hearing Disorders diagnosis
Hearing Tests statistics & numerical data
Machine Learning
Subjects
Details
- Language :
- English
- ISSN :
- 1708-8186
- Volume :
- 58
- Issue :
- 4
- Database :
- MEDLINE
- Journal :
- International journal of audiology
- Publication Type :
- Academic Journal
- Accession number :
- 30900518
- Full Text :
- https://doi.org/10.1080/14992027.2018.1554912