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In conclusion, we recognize the difficulties inherent in interpreting causes and mechanisms responsible for CRC-associated TP53 mutations, which are the end result of complex cascades of events.
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A certain amount of caution should be exercised when interpreting cause-specific changes over time.
One must remain particularly circumspect in interpreting cause-of-death statistics of the start of the 20th century.
Currently, the InterVA model may be the only freely available computer-based automated method for interpreting cause of death at the population level using VA data (23).
A new version of the InterVA model for interpreting cause of death from VA data was also released in 2012 (8), which exactly corresponds to the WHO 2012 VA standard in terms of VA questions and cause of death categories.
Despite its computational simplicity, it is certainly true that using any mathematical model for interpreting cause of death may not reflect the subjective subtleties of physician review, barring inconsistent physician reviews.
Full details of the InterVA model, including its use in interpreting cause-of-death data from HDSS across the developing world, have also been described in previous publications (28– 38).
The potential disadvantage of using any mathematical model for interpreting cause of death is that some subtlety and nuance may be lost as compared to routine physician certification of cause of death (either from attending physicians or from physicians coding individual VA cases).
At the present the basic concept about individuals' ability to interpret causes that explain the self experienced functional limitations is not completely understood.
Oti et al. compared the Spectrum model outputs for the Nairobi area against the health and demographic surveillance site (HDSS) observed mortality using InterVA-4 to interpret cause of death.
It should be noted that although we used inter-VAm to interpret cause of death using information from the MADE-FOR, to calculate the MMR we used PRD definition for the numerator.
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