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In this article, we propose a method for grouping trajectories of care over a sequence of hospitalizations, using claim data.
They proposed a method for grouping care pathways over a sequence of hospitalizations using claim data [ 18].
Step 3 consisted in building the one year care trajectory for each patient identified at step 2. This trajectory is defined as the sequence of hospitalizations beginning less than 366 days after the index stay, which is the first element of the trajectory.
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All eligible patients were assigned a serial number according to the consecutive sequence of hospitalization and allocated to group U (using unipedicular approach) or group B (using bipedicular approach) randomly by computer.
A compelling motivation for modeling time to first and time between hospitalizations, as opposed to binary regression models of any hospitalization over a fixed time interval, is that the data are a sequence of intervals of differing lengths separated by hospitalizations or occasions when a resident's covariates are observed to have changed.
Ten more surgeries followed, months of hospitalizations, years of rehabilitation.
Reports of hospitalizations and deaths from flu are higher.
– Number of hospitalizations.
**Hospitalization rates (number of hospitalizations per 1000 cases of HZ).
For hospitalization, the indications and number of hospitalizations were recorded.
†Lethality = no. of deaths / no. of hospitalizations.
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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