Literature DB >> 24280850

Am I right when I am sure? Data consistency influences the relationship between diagnostic accuracy and certainty.

Rodrigo B Cavalcanti1, Matthew Sibbald.   

Abstract

PURPOSE: When gauging diagnostic accuracy cognitive biases may lead to inaccurate estimates of certainty, predisposing clinicians to diagnostic errors. This study explored the relationship between diagnostic accuracy and measures of certainty for diagnoses based on consistent or inconsistent information.
METHOD: The authors analyzed three experiments among 180 to 190 postgraduate trainees performing cardiac physical diagnoses using a simulator from 2010 to 2012. Each asked participants to assess diagnostic certainty. One experiment used a seven-point certainty scale and provided only simulated physical findings. Two assessed certainty continuously (probability 1%-100%) and included cases with inconsistent clinical information in addition to simulated physical findings. Relationships between certainty and accuracy were explored through descriptive statistics and nonparametric tests.
RESULTS: Measures of certainty ranged widely (between 2 and 7, and 5%-100%). Relationships between accuracy and certainty varied depending on information consistency. In experiments providing only simulated findings, or consistent clinical data, diagnostic accuracy was associated with higher certainty (median 90% versus 75%, and 5/7 versus 4/7, both P < .001). Studies providing inconsistent data generated similar certainty among participants regardless of accuracy (median 75% versus 75%, P = .36; and 80% versus 85%, P = .60).
CONCLUSIONS: Diagnostic accuracy was moderately associated with higher certainty only when clinical data were consistent. This correlation disappeared when incon sistent data were provided, possi bly reflecting changes in reasoning strategies among diagnostically success ful trainees. The relationship between certainty and diagnostic accuracy is context dependent. Certainty is an unreliable surrogate for diagnostic accuracy.

Entities:  

Mesh:

Year:  2014        PMID: 24280850     DOI: 10.1097/ACM.0000000000000074

Source DB:  PubMed          Journal:  Acad Med        ISSN: 1040-2446            Impact factor:   6.893


  6 in total

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Authors:  Heather T Keenan; Lawrence J Cook; Lenora M Olson; Tyler Bardsley; Kristine A Campbell
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2.  Image interpretation: Learning analytics-informed education opportunities.

Authors:  Elana Thau; Manuela Perez; Martin V Pusic; Martin Pecaric; David Rizzuti; Kathy Boutis
Journal:  AEM Educ Train       Date:  2021-04-01

3.  Perceived social risk in medical decision-making for physical child abuse: a mixed-methods study.

Authors:  Heather T Keenan; Kristine A Campbell; Kent Page; Lawrence J Cook; Tyler Bardsley; Lenora M Olson
Journal:  BMC Pediatr       Date:  2017-12-22       Impact factor: 2.125

Review 4.  Monitoring and regulation of learning in medical education: the need for predictive cues.

Authors:  Anique B H de Bruin; John Dunlosky; Rodrigo B Cavalcanti
Journal:  Med Educ       Date:  2017-03-23       Impact factor: 6.251

5.  How well do final year undergraduate medical students master practical clinical skills?

Authors:  Sylvère Störmann; Melanie Stankiewicz; Patricia Raes; Christina Berchtold; Yvonne Kosanke; Gabrielle Illes; Peter Loose; Matthias W Angstwurm
Journal:  GMS J Med Educ       Date:  2016-08-15

6.  Implicit expression of uncertainty - suggestion of an empirically derived framework.

Authors:  Julia Gärtner; Pascal O Berberat; Martina Kadmon; Sigrid Harendza
Journal:  BMC Med Educ       Date:  2020-03-20       Impact factor: 2.463

  6 in total

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