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For example, the story of 40,000 Iraqis stranded and starving on a mountain – invoked by supporters of intervention – turned out to be exaggerated.
The automatic parametrisation by identifying clusters without user intervention turned out to be essential to process a large amount of multiantenna measurement data.
Overall satisfaction among users of the intervention turned out to be good.
This means that even if the intervention turned out to be effective based on the primary outcome it would not be likely to be cost-effective.
The type of feedback provided to the general practitioner about both the global geriatric assessment and the result of the intervention turned out to be the most discriminant component between the types.
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If the intervention turns out to be (cost-) effective we recommend implementation in regular health care.
If this intervention turns out to be safe and effective it could have a profound impact on improving pregnancy outcomes and lowering health care costs.
Whether or not the intervention turns out to have effect upon the primary outcome, it will be important to establish hypotheses upon why the intervention was (or was not) effective.
To study this process evaluations are performed to recognize strong and weak aspects of an intervention, to explain observed (lack of) intervention effects, and to further improve an intervention prior to implementation, i.e. if an intervention turns out to be effective [ 16, 17].
Accordingly, the two interventions turned out to be additive in their effect (Fabel et al. 2009).
And they must be designed in a way that recognizes that not all innovative interventions turn out to be safe and effective treatments.
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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