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The average recall score of participants who learned and recalled in the tutorial room was 13.0 +/− 3.84 (mean, SD).
The average recall score of participants who learnt and recalled in the operating theatre was 12.92 +/− 4.18 (mean, SD), representing no significant difference between the two environments for learning (p=0.4792).
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There was no statistical difference seen between the average recall scores of any of the four word lists.
In addition, the differences in average recall scores between baseline and follow-up were greater in intervention city for all campaign activities.
The conventional schema produced average recall and precision scores of 0.59 and 0.71.
For the prevention data, Semantic MEDLINE enhanced with dynamic summarization achieved average recall and precision scores of 0.655 and 0.329.
Both dynamic summarization and conventional summarization outperformed the baseline method, which produced average recall and precision scores of 0.23 and 0.31.
Including findings from both drug treatment and prevention analyses, Combo produced average recall and precision scores of 0.75 and 0.35, while the baseline method yielded average recall and precision values of 0.25 and 0.28.
For the drug treatment data, Semantic MEDLINE enhanced with dynamic summarization achieved average recall and precision scores of 0.848 and 0.377, while conventional summarization produced 0.583 average recall and 0.712 average precision, and the baseline method yielded average recall and precision values of 0.252 and 0.277.
A more detailed description can be found in Figure 5. Figure 7 Average-ranked ASes with very good recall score.
The recall is computed as a/k, where k is the total number of pathway genes that were scored by the MSGR. The average of the precision and recall is taken across the cross-validation folds to obtain an average precision and an average recall for a single pathway.
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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