Literature DB >> 9988038

A multi-media, computer-based method for stroboscopy rating training.

B J Poburka1, D M Bless.   

Abstract

Methods of training individuals to rate stroboscopic examinations vary widely in rating criteria, viewing times, samples, and length of training. Consequently, problems occur in both inter- and intrajudge agreement. Computer-aided instruction (CAI) provides a means to integrate and control key learning factors that facilitate learning. This study attempted to determine if CAI could train individuals to make accurate and reliable visuo-perceptual judgments of stroboscopy. Experienced and inexperienced subjects rated 45 samples before and after training. Following 4 to 5 hours of CAI training, the subjects with no previous experience demonstrated improved interjudge agreement with a panel of expert raters. The training was not effective for the experienced group. Regardless of the rater's experience, the parameters that required evaluation of movement were more difficult to rate than those requiring only an assessment of structure.

Mesh:

Year:  1998        PMID: 9988038     DOI: 10.1016/s0892-1997(98)80060-x

Source DB:  PubMed          Journal:  J Voice        ISSN: 0892-1997            Impact factor:   2.009


  4 in total

Review 1.  Rater methodology for stroboscopy: a systematic review.

Authors:  Heather Shaw Bonilha; Kendrea L Focht; Bonnie Martin-Harris
Journal:  J Voice       Date:  2014-09-26       Impact factor: 2.009

2.  Current role of stroboscopy in laryngeal imaging.

Authors:  Daryush D Mehta; Robert E Hillman
Journal:  Curr Opin Otolaryngol Head Neck Surg       Date:  2012-12       Impact factor: 2.064

3.  Parameters and Scales Used to Assess and Report Findings From Stroboscopy: A Systematic Review.

Authors:  Heather Shaw Bonilha; Maude Desjardins; Kendrea L Garand; Bonnie Martin-Harris
Journal:  J Voice       Date:  2017-11-02       Impact factor: 2.009

4.  BAGLS, a multihospital Benchmark for Automatic Glottis Segmentation.

Authors:  Pablo Gómez; Andreas M Kist; Patrick Schlegel; David A Berry; Dinesh K Chhetri; Stephan Dürr; Matthias Echternach; Aaron M Johnson; Stefan Kniesburges; Melda Kunduk; Youri Maryn; Anne Schützenberger; Monique Verguts; Michael Döllinger
Journal:  Sci Data       Date:  2020-06-19       Impact factor: 6.444

  4 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.