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The assessment of spatial variability in such errors is also essential for areas with local topographic variations (Heritage et al. 2009; Wheaton et al. 2010; Milan et al. 2011).
Among the 89 drugs cited as involved in such errors, the most frequent where atropine (107 times, 24% of responders), adrenaline (94 times, 21%), ephedrine (75 times, 17%), NaCl vs KCl vs aqua pro injectione (17%), xylocaine (11%) and morphine (7%).
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Preserved in perpetuity, such errors cast doubt on the reliability of a paper's scientific content.
In contrast, such errors do not occur when measuring at broad webs.
In hindsight, such errors are unsurprising.
In practice, such errors would typically not be spotted or rectified by other members of the team.
Furthermore, whereas underreporting and selective scanning may affect estimates at a single point in time, such errors are less relevant if tracking purchasing over time.
In our 23 samples, application of the QD filter to putative indels detected by the TS3.4 + SAMtools workflow removed 75% of the non-homopolymer-associated errors, thus demonstrating the usefulness of the QD filter in minimising such errors.
But it is hard to imagine that many readers would be badly misled by this table, and in any event, such errors are minor in comparison to the error of using a demonstrably invalid test.
That the present study was conducted in critically ill individuals, all of whom had a bladder catheter in place, makes such errors less likely.
Quality checks such as the ones implemented in UniChem hinder such errors from being propagated to local databases for annotation applications.
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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