Results 181 to 190 of about 201,890 (307)
Correction: Goto, Y.; Miura, H. Evaluation of an Advanced Care Planning Training Program Incorporating Online Skills in Shared Decision Making: A Preintervention and Postintervention Comparative Study. Healthcare 2023, 11, 1356. [PDF]
Goto Y, Miura H.
europepmc +1 more source
A distinct semi‐confined inner‐tube chemical vapor deposition geometry enables reproducible, large‐area growth of phase‐pure 2D β′‐In2Se3 from InI + Se precursors. Engineering local vapor transport and optimizing precursor delivery and temperature–time conditions yield uniform continuous films.
Dasun P. W. Guruge +8 more
wiley +1 more source
Multi Components in the Implementation of Advanced Care Planning for Patients with Cancer: A Scoping Review. [PDF]
Yodang Y +3 more
europepmc +1 more source
This study shows that superalloys used in aircraft engine disks become much more prone to deformation at high temperatures if they have been strained during manufacturing. This effect increases with the level of prior strain but eventually reaches a limit.
Fabio Machado Alves da Fonseca +9 more
wiley +1 more source
EHR-Based Advanced Care Planning and Late-Stage Cancer Treatment in a Middle-Income Country: A Retrospective Cohort Study. [PDF]
Leal MH +4 more
europepmc +1 more source
Patients From Distressed Communities Who Undergo Surgery for Hip Fragility Fractures Are Less Likely to Have Advanced Care Planning Documents in Their Electronic Medical Record. [PDF]
Khan IA +6 more
europepmc +1 more source
Low‐voltage FIB‐SEM tomography combined with a image preprocessing pipeline improves phase contrast and enables reliable machine‐learning segmentation of conductive networks in lithium‐ion battery electrodes. Structural descriptors are extracted from segmented images, done semimanually and automated, and compared.
Lisa Beran +6 more
wiley +1 more source
Advance care planning matters [PDF]
openaire +2 more sources
Advance care planning: Not a panacea [PDF]
Rietjens, Judith +2 more
openaire +3 more sources
This study applies machine learning regression to predict chromium layer thickness in decorative trivalent chromium electroplating, using 441 experiments from laboratory‐scale (1L) and pilot‐scale (14L) setups. Tree‐based models, particularly CatBoost, outperformed linear regression by capturing nonlinear parameter interactions (R2$R^2$ up to 0.77 ...
Christoph Baumer +4 more
wiley +1 more source

