{"id":{"repo_id":"umn","oai_identifier":"oai:conservancy.umn.edu:11299/218671"},"canonical_url":"https://search.dev.ndltd.org/etd/umn/oai:conservancy.umn.edu:11299/218671","repository":{"repo_id":"umn","name":"University of Minnesota","base_url":"https://conservancy.umn.edu/server/oai/request"},"display":{"title":"Human Versus Computer Algorithmic Measurements of Caloric Response: Implications for Test Analysis","abstract":"Objectives: To quantify variation introduced to bithermal caloric test (BCT) analysis by data cleaning and determine how variation differs between examiners. Methods: Analysis of 435 consecutive BCTs performed by 6 examiners using identical protocols on adults with dizziness. Outcomes of total eye speed (TES) and unilateral weakness (UW%) were compared between examiner-modified tracings and automated algorithms. Results: Algorithms erroneously selected artifact in 9.7% of tests. Examiner cleaning resulted in a mean change in TES of (-)4deg/sec (95%CI 3-4, p<0.001) but no change in UW%. Limits of agreement (Bland-Altman analyses) for TES were (-)20 to (+)8deg/sec and for UW (-)10 to 10% and varied between examiners. Algorithms had 15% false negative and 2% false positive rates. Conclusions: Data cleaning may reduce the rate of false negative results. Differences in cleaning methods may produce test-retest and inter-individual variation and alter lab-derived normative values. Consensus is needed regarding optimal data cleaning methods.","abstract_html":"Objectives: To quantify variation introduced to bithermal caloric test (BCT) analysis by data cleaning and determine how variation differs between examiners. Methods: Analysis of 435 consecutive BCTs performed by 6 examiners using identical protocols on adults with dizziness. Outcomes of total eye speed (TES) and unilateral weakness (UW%) were compared between examiner-modified tracings and automated algorithms. Results: Algorithms erroneously selected artifact in 9.7% of tests. Examiner cleaning resulted in a mean change in TES of (-)4deg/sec (95%CI 3-4, p&lt;0.001) but no change in UW%. Limits of agreement (Bland-Altman analyses) for TES were (-)20 to (+)8deg/sec and for UW (-)10 to 10% and varied between examiners. Algorithms had 15% false negative and 2% false positive rates. Conclusions: Data cleaning may reduce the rate of false negative results. Differences in cleaning methods may produce test-retest and inter-individual variation and alter lab-derived normative values. Consensus is needed regarding optimal data cleaning methods.","abstract_has_math":false,"creators":["Adams, Meredith"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018-12","date_published":"2018-12","updated_at":"2026-07-24T05:19:50Z","subjects":["caloric test","computer algorithm","unilateral weakness","variation"],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/11299/218671","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Adams, Meredith"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2021-02-22T15:29:51Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2021-02-22T15:29:51Z"]},{"key":"dc:date.issued","label":"Date","values":["2018-12"]},{"key":"dc:type","label":"Dc Type","values":["Thesis or Dissertation"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["caloric test","computer algorithm","unilateral weakness","variation"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/11299/218671"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["University of Minnesota M.S. thesis. December 2018. Major: Clinical Research. Advisor: Bevan Yueh. 1 computer file (PDF); iv, 30 pages."]},{"key":"dc:description.abstract","label":"Abstract","values":["Objectives: To quantify variation introduced to bithermal caloric test (BCT) analysis by data cleaning and determine how variation differs between examiners. Methods: Analysis of 435 consecutive BCTs performed by 6 examiners using identical protocols on adults with dizziness. Outcomes of total eye speed (TES) and unilateral weakness (UW%) were compared between examiner-modified tracings and automated algorithms. Results: Algorithms erroneously selected artifact in 9.7% of tests. Examiner cleaning resulted in a mean change in TES of (-)4deg/sec (95%CI 3-4, p<0.001) but no change in UW%. Limits of agreement (Bland-Altman analyses) for TES were (-)20 to (+)8deg/sec and for UW (-)10 to 10% and varied between examiners. Algorithms had 15% false negative and 2% false positive rates. Conclusions: Data cleaning may reduce the rate of false negative results. Differences in cleaning methods may produce test-retest and inter-individual variation and alter lab-derived normative values. Consensus is needed regarding optimal data cleaning methods."]},{"key":"dc:title","label":"Title","values":["Human Versus Computer Algorithmic Measurements of Caloric Response: Implications for Test Analysis"]}]}],"canonical_facts":{"dc:creator":["Adams, Meredith"],"dc:date.accessioned":["2021-02-22T15:29:51Z"],"dc:date.available":["2021-02-22T15:29:51Z"],"dc:date.issued":["2018-12"],"dc:description":["University of Minnesota M.S. thesis. December 2018. Major: Clinical Research. Advisor: Bevan Yueh. 1 computer file (PDF); iv, 30 pages."],"dc:description.abstract":["Objectives: To quantify variation introduced to bithermal caloric test (BCT) analysis by data cleaning and determine how variation differs between examiners. Methods: Analysis of 435 consecutive BCTs performed by 6 examiners using identical protocols on adults with dizziness. Outcomes of total eye speed (TES) and unilateral weakness (UW%) were compared between examiner-modified tracings and automated algorithms. Results: Algorithms erroneously selected artifact in 9.7% of tests. Examiner cleaning resulted in a mean change in TES of (-)4deg/sec (95%CI 3-4, p<0.001) but no change in UW%. Limits of agreement (Bland-Altman analyses) for TES were (-)20 to (+)8deg/sec and for UW (-)10 to 10% and varied between examiners. Algorithms had 15% false negative and 2% false positive rates. Conclusions: Data cleaning may reduce the rate of false negative results. Differences in cleaning methods may produce test-retest and inter-individual variation and alter lab-derived normative values. Consensus is needed regarding optimal data cleaning methods."],"dc:identifier.uri":["https://hdl.handle.net/11299/218671"],"dc:language.iso":["en"],"dc:subject":["caloric test","computer algorithm","unilateral weakness","variation"],"dc:title":["Human Versus Computer Algorithmic Measurements of Caloric Response: Implications for Test Analysis"],"dc:type":["Thesis or Dissertation"]},"updated_at":"2026-07-24T05:19:50Z"}