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All items achieved a Cronbach alpha greater than 0.8.
Twenty seven of the 32 items achieved a CVI greater than 0.79 (range 0.88 1.0).
The score (range: 0 4; mean: 2.6 ± 1.4) evaluating the need for further education, calculated on the 4 preceding items, achieved a Cronbach α of 0.75.
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The algorithm was implemented and evaluated using several corpora of actual news items, achieving an average accuracy of around 96%.
For example, Brann et al [ 63] reported a particularly low intra-class correlation (0.06) for Item 10 (Problems with peer relationships), but Gowers et al [ 3, 4] found that this item achieved an intra-class correlation of 0.77.
Moreover, all items achieved strong consensus in an anonymous voting process, with the lowest result being a mean of 9.0 on a scale of 0 10, indicating that the task force stood quite united behind the recommendations.
The total item correlation was evaluated, and items that achieved a score of ≥ 0.5 were accepted [ 39].
When the four categories of importance were merged into the two categories of low to moderate importance (<7) and high to very high importance (≥7), a further 16 items achieved consensus agreement (Table 3).
Here, participants who took a test consisting of automatically generated items achieved scores that highly correlated with their scores in the Test of English for International Communication (TOEIC).
Lipton et al. presented a very short screening questionnaire for MIG, with only three items, and achieved a sensitivity of 0.81 and a specificity of 0.75, with a kappa coefficient of 0.68 [11].
The most important items achieved in these investigations include time, accuracy, speed of drug injection and cost reduction.
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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