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In addition, we observed concomitant early (lag hour 2) trends toward conduit artery vasodilatation (increased BAD) and impairment in endothelial function (decreased FMD).
We found a statistically significant increased risk of PAF associated with mean O3 concentration in the concurrent hour (lag hour 0; Table 2).
Conditional logistic regression models, including the mean pollutant concentration in the hour of the arrhythmia (lag hour 0) and natural splines [3 degrees of freedom (df)] for the mean temperature, dew point, and barometric pressure in the 24 hr before the arrhythmia, were run separately for each pollutant (PM2.5, black carbon, NO2, CO, SO2 and O3).
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The limitation of using a single-lag model is that the estimated PM2.5 effect at each of the lag hours could be confounded by the effect of other lag hours.
We considered associations with longer exposures before the PAF episode using the mean of the pollutant in the previous 24 hr (lag hours 0 23).
Analyses were done for lag 0 (hour of arrest), lag 1 (hour before arrest), lag 2, and so on, and average concentrations of lag 0 2 (average of hour of arrest, lag 1, and lag 2), lag 0 3, lag 0 4, lag 0 8, lag 0 12, lag 0 24, and lag 0 48.
Because of the differences in onset errors in LD, we tested how Prkg1BKO mutant and control mice reacted to chronic jet lag (4 hour advance every 2 days).
This association was also seen during the period from lag 23-hour to lag 12-hour, i.e., the period from 7 p.m. of the previous day to 7 a.m. of the same day in which PEF were measured.
A decline in PEF at 7 p.m. was associated with the hourly concentration of PM2.5 during the period from lag 3-hour to lag 0-hour, i.e., the period from 3 p.m. to 7 p.m. of the same day on which PEF was measured.
A decline in PEF at 7 a.m. was associated with the hourly concentration of PM2.5 during the period from lag 15-hour to lag 4-hour, i.e., the period from 3 p.m. of the previous day to 3 a.m. of the same day in which PEF was measured.
Because the PM2.5 and ECG variables were assessed in parallel over 48 lags (24 hour), we decided a prior to model no more than ten lags, which allowed us to fit the distributed lag model using at least 75% of the data.
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