The phrase "adjusted for features" is correct and usable in written English.
It can be used in contexts where you are discussing modifications or changes made to account for specific characteristics or attributes of something.
Example: "The data was adjusted for features such as age and income to ensure accurate analysis."
Alternatives: "accounting for characteristics" or "modified for attributes".
To compare CMP patients who are users and non-users of A/C care on their health care utilization, we will need to adjust for features that might be associated with both the use of CAM (A/C care) and health care utilization overall.
However, the analyses were not adjusted for structural features.
Using Cox regression, we calculated hazard ratios (HRs) for death according to the year of diagnosis from 2004/2005 to 2010; adjusted for patient features including age, sex and co-morbidity.
This result was further confirmed in multivariate regression (P = 0.021) (data not shown) adjusted for the features that were different at baseline (see Table 1): KL grade, presence of BML, and JSW.> -wrap-foot> Results for cartilage volume loss are percentage of change expressed as mean ± SD.
Cluster importance score was defined by the selection frequencies of the individual clinical features adjusted for the number of clinical features in a cluster.
We then adjusted for other demographic features that might confound the association of poor fetal growth and gastroschisis.
When adjusted for cardiovascular imaging features, only ground glass, pleural effusion and lymphadenopathy were demonstrated to be independently associated with CVD event risk.
Preoperative PSA and primary Gleason pattern were not found to be statistically significant; however, multivariate analyses were adjusted for all clinicopathologic features including preoperative PSA and primary Gleason pattern based on established prognostic factors for BF [ 19]. *Non-Localized Tumor Indicator is a summary metric of tumor confined to prostate.
After adjusting for sociodemographic features (Model 1), the odds of transformation were elevated in persons with depression (OR = 3.22; 95 % CI 1.65 6.25).
Adjusting for the features in Model 2, results for WAs persisted, although effect sizes were expectably lower.
