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In the case of feedback-free DSC coding [3, 4, 11], the code has to be designed to cope with the relatively large variation in number of errors in case of short blocks.
Accuracy was high; mean number of errors summed across all conditions was 1.2, and no differences in number of errors between groups or conditions were found (F < 1, NS).
Preoperatively there was no difference between groups in number of errors made before attaining criterion performance on either visual discrimination (D1: F = 1.139, D2: F < 1, see Table 3).
For the data obtained in the standard form of the pegboard test there was a significant correlation between the initial number of errors and the reduction in number of errors after CA (n = 16, r = −0.921, p ≤ 0.001), but not for the time to fulfill the test and the accordant gain (n = 16, r = −0.100, p = 0.713).
We did not obtain any significant differences between the tasks in number of errors [commission errors: z = 0.90, p = 0.37, d = 0.20; commission errors O-not-X type: z = 0.98, p = 0.33, d = 0.18; omission errors: z = -0.72, p = 0.47, d = 0.11].
Both control and UCN3OE mice showed similar learning in the Barnes maze (39), as shown by measures of reduction in distance of the first hole checked from the escape hole (first error), reduction in number of errors made in each trial set, shorter latency to escape over 16 trials, and the strategy used to locate the escape hole.
Similar(54)
where is the bit error rate at the given and is the error correction capability (in number of bit errors).
Further accumulation of material is need to prove reduction in number of medical errors.
There was no difference in number of perseverative errors between persons with low or high educational level: median (IQR), 7 (3 to 13) and 7 (4 to 13), respectively (p = 0.50).
There was a correlation between increased versican and an increase in the number of errors in the final block (block 12) of the RAWM in the Ts65Dn stratum oriens (P = 0.0044, r = 0.9912), but not LMC stratum oriens.
Significant differences emerged between AN and BED patients compared to HC in the number of errors in Part B.
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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