A Randomized Controlled Trial of the Implementation of BREASTChoice , a Multilevel Breast Reconstruction Decision Support Tool with Personalized Risk Prediction.
ANNALS OF SURGERY(2025)
Washington Univ
Abstract
Objective:To implement the BREASTChoice decision tool into the electronic health record and evaluate its effectiveness.Background: BREASTChoice, is a multilevel decision tool that (1) educates patients about breast reconstruction, (2) estimates personalized risk of complications, (3) clarifies patient preferences, and (4) informs clinicians about patients' risk and preferences.Methods:A multisite randomized controlled trial enrolled adult women with stage 0 to III breast malignancy undergoing mastectomy. Participants were randomized to BREASTChoice or a control website. A survey assessed knowledge, preferences, decisional conflict, shared decision-making, preferred treatment, and usability. We conducted intent-to-treat (ITT), per-protocol (PP) analyses (those randomized to BREASTChoice who accessed the tool), and stratified analyses.Results:A total of 23/25 eligible clinicians enrolled. A total of 369/761 (48%) contacted patients enrolled and were randomized. Patients' average age was 51 years; 15% were older than 65. BREASTChoice participants had higher knowledge than control participants (ITT: mean 70.6 vs 67.4, P=0.08; PP: mean 71.4 vs 67.4, P=0.03), especially when stratified by site (ITT: P=0.04, PP: P=0.01), age (ITT: P=0.04, PP: P=0.02), and race (ITT: P=0.04, PP: P=0.01). BREASTChoice did not improve decisional conflict, match between preferences and treatment, or shared decision-making. In PP analyses, fewer high-risk patients using BREASTChoice chose reconstruction. BREASTChoice had high usability.Conclusions: BREASTChoice is a novel decision tool incorporating risk prediction, patient education, and clinician engagement. Patients using BREASTChoice had higher knowledge; older adults and those from racially minoritized backgrounds especially benefitted. There was no impact on other decision outcomes. Future studies should overcome implementation barriers and specifically examine decision outcomes among high-risk patients.
MoreTranslated text
Key words
shared decision-making,clinical decision support,breast reconstruction,breast cancer
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined