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Codes were missing in 6.2% of the problem events; incorrect codes were observed in 4.0% of the problem events and text mismatch between the diagnoses and the expected ICPC-2 diagnoses text in 53.8% of the problem events.
Also, the percentage of problem events with correct ICPC-2 codes was 89.8%.
A mismatch appeared in 4.0% of the total problem events (Table 3).
Mean, median and range were calculated per group of problem events.
The physicians created many 'new' diagnoses and assigned them to problem events.
However, the problem events that were investigated (approximately 1.5 million) reflect the demands for diagnoses and codes over a huge number of problem events and for a long period of study; the number of physicians involved is also sufficient.
Similar(22)
CONSIDER this logic problem: Event A causes Event B. B then causes C, and C causes D. Now assume A had never happened.
Each record in the narrative module defines one problem event (single coloured boxes; Figure 1).
The ICPC codes and diagnoses defined a problem event for each patient in the PROblem-oriented electronic MEDical record (PROMED).
To better understand how a simple expansion tree is computed, we show in Figure 1 the simple expansion tree created for the relation TeRP between TEST event 'evaluated' and PROBLEM event 'desaturate'desaturate
In this sentence, there is a TeCP relation between the TEST event 'bone marrow biopsy' and the PROBLEM event 'persistent pancytopenia', meaning that bone marrow biopsy is conducted to investigate persistent pancytopenia.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com