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The aim of this MRM study was to design and to test robustness of Artificial Neural Network architectures to predict malignancy using a large clinical database.
Here, we investigate the potential role of five pharmacokinetic genes on the response to and tolerance of citalopram using a large clinical sample of depressed patients who were enrolled in the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study [15].
To test this hypothesis, we performed an observational study using a large clinical database of unselected adult critically ill patients.
Using a large, clinical research database (CareFusion), we identified patients hospitalized at 97 hospitals in the U.S. between 2003 and 2007 for culture-documented diabetic foot infection.
This was achieved using a large clinical dataset, comprising long unedited multi-channel EEG recordings from 18 neonates with seizures and 20 neonates without seizures, totalling 1479 h of multi-channel EEG in duration and with 1389 seizures.
94 Catheter displacement can be corrected by changing source dwell positions, or by physical readvancement of catheters before treatment; it may also be compensated for by using a large clinical target volume margin.
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Our study used a large clinical cohort with data collection and follow-up spanning over a decade.
We were able to use a large clinical dataset with a number of measured characteristics controlled for with multivariable modeling.
In conclusion, we used a large clinical database to develop and validate a risk score that seems to accurately stratify LEA risk among patients hospitalized for a diabetic foot infection.
To study the effects of accounting for missing data and incorporating model stability we used a large clinical data set in which we empirically evaluated different methods of deriving a prognostic model.
We performed a 6-year retrospective analysis of over 120,000 ICU admissions to 57 ICUs across ANZ, using a large validated clinical database, to evaluate the rate, clinical characteristics, outcomes and projected resource demand of very old patients (aged ≥ 80 years) admitted to the ICU.
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CEO of Professional Science Editing for Scientists @ prosciediting.com