Literature DB >> 576054

Olfactory testing: rules for odor identification.

W S Cain, R J Krause.   

Abstract

Neurology manuals generally recommend odor identification for simple assessment of olfaction. Nevertheless, even patients with normal olfaction (normosmics) often perform only poorly. Three experiments demonstrate that such an ambiguous outcome will disappear if the test incorporates highly familiar substances and, more important, a procedure to circumvent the olfactory-verbal gap that frequently separates an odor from its name. One multiple-choice procedure, for instance, led to 100% accuracy among normosmics. Another led to 99% accuracy among normosmics and 0% accuracy among anosmics. The investigation also reveals that scratch-and-sniff labels could possibly replace customary odorants in the clinical test.

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Year:  1979        PMID: 576054     DOI: 10.1080/01616412.1979.11739536

Source DB:  PubMed          Journal:  Neurol Res        ISSN: 0161-6412            Impact factor:   2.448


  9 in total

Review 1.  Olfaction in persons with Alzheimer's disease.

Authors:  M D Thompson; K Knee; C J Golden
Journal:  Neuropsychol Rev       Date:  1998-03       Impact factor: 7.444

Review 2.  The muted sense: neurocognitive limitations of olfactory language.

Authors:  Jonas K Olofsson; Jay A Gottfried
Journal:  Trends Cogn Sci       Date:  2015-05-12       Impact factor: 20.229

3.  Sumner's "On testing the sense of smell" revisited.

Authors:  W S Cain
Journal:  Yale J Biol Med       Date:  1982 Sep-Dec

4.  Machine-learned pattern identification in olfactory subtest results.

Authors:  Jörn Lötsch; Thomas Hummel; Alfred Ultsch
Journal:  Sci Rep       Date:  2016-10-20       Impact factor: 4.379

5.  Development of Chinese odor identification test.

Authors:  Baihan Su; Dawei Wu; Yongxiang Wei
Journal:  Ann Transl Med       Date:  2021-03

Review 6.  The impact of expertise in olfaction.

Authors:  Jean-Pierre Royet; Jane Plailly; Anne-Lise Saive; Alexandra Veyrac; Chantal Delon-Martin
Journal:  Front Psychol       Date:  2013-12-13

7.  Cross-Cultural Administration of an Odor Discrimination Test.

Authors:  Agnieszka Sorokowska; Piotr Sorokowski; Thomas Hummel
Journal:  Chemosens Percept       Date:  2014-04-22       Impact factor: 1.833

Review 8.  Machine Learning in Human Olfactory Research.

Authors:  Jörn Lötsch; Dario Kringel; Thomas Hummel
Journal:  Chem Senses       Date:  2019-01-01       Impact factor: 3.160

9.  A machine-learned analysis suggests non-redundant diagnostic information in olfactory subtests.

Authors:  Jörn Lötsch; Thomas Hummel
Journal:  IBRO Rep       Date:  2019-01-07
  9 in total

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