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Awareness and communication of uncertainty/confidence in your diagnosis is very important to help you become aware of diagnostic error. Please rate your uncertainty/confidence for your most probable diagnosis.
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This is a new case. The case is ready for students to work on, but more work is needed on the scorecard before stats can be released to students. Please email Dr. Waechter at jwaech@gmail.com so he can update the scorecard when there is more student data.
Total errors demonstrated in this case: Number of learners who demonstrated at least 1 error:
This error occurs if you accept a diagnosis as the "final most probable diagnosis" without collecting or assigning the confirmatory (gold standard) data. If the following diagnoses were ranked as "most probable" for the case AND the required data was not assigned as "INCREASES" probability, then this behavior is consistent with premature closure error.
This error occurs if you reject a diagnosis as probable without collecting or assigning the confirmatory (gold standard) data. If the following diagnoses were either NOT included in the Ddx, or if they were included but did not have the following data assigned as "DECREASES" probability, then this behavior is consistent with a failure to rule out error.
This error occurs if you choose a diagnosis early in the diagnostic process that was not the correct diagnosis, and indicated this diagnosis as the most probable diagnosis at least 2 out of 3 times during the case and again as the final diagnosis the end of the case.
This error typically occurs in association with the Anchoring error. If there is key data that would effectively make a diagnosis highly unlikely AND that data is not assigned as "DECREASES" probability AND this diagnosis was chosen as the most probable diagnosis, then this behavior is consistent with confirmation error.
This error typically occurs in association with the search satisficing error.
Penalties for Investigations:
Not using data penalty:
Ordering too many tests or rounds penalty:
Total penalties:
No data was assigned to your differential diagnosis from any of these investigations:
Each test has a financial cost, a time cost to the patient, and some tests carry risks to the patient. It is important to avoid ordering too many tests. Also, each "round" of tests adds time to the diagnostic process and should be minimized as well.
This error occurs if you accept a diagnosis as the "final most probable diagnosis" without collecting the confirmatory data (in other words, the ordering the gold standard test). If the following diagnoses were ranked as "most probable" for the case AND the required confirmatory data was not assigned as "INCREASES" probability, then the learner will be notified of behavior consistent with premature closure error.
This error occurs if you reject a diagnosis as probable without collecting the confirmatory data (in other words, the ordering the gold standard test). If the following diagnoses were either NOT included in the Ddx, or if they were included but did not have the following data assigned as "DECREASES" probability, then the learner will be notified of behavior consistent with failure to rule out worst case scenario.
This error typically occurs in association with the Anchoring Error. If there is key data that would effectively make a diagnosis highly unlikely AND that data is not assigned as "DECREASES" probability, then this behavior is consistent with confirmation error.
This error typically occurs in association with the Search Satisficing Error. If there is key data that would effectively make a diagnosis highly unlikely AND that data is not assigned as "DECREASES" probability, then this behavior is consistent with confirmation error.
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This error occurs if you choose a diagnosis early in the diagnostic process and despite that diagnosis not being the true most probable diagnosis, you keep that diagnosis as the most probable diagnosis throughout most, or all of the case. There is an automated algorithm to identify anchoring error. No work is required on the part of the case author to generate this feedback.