Improved Uptake and Adherence to Risk-Reducing Medication with the Use of Low-Dose Tamoxifen in Patients at High Risk for Breast Cancer.
CANCER PREVENTION RESEARCH(2024)
AdventHealth | Mayo Clin
Abstract
Women at increased risk for breast cancer may benefit from taking risk-reducing medication (RRM) with tamoxifen (tam). Historical uptake of tam in women who qualify has been low. Recent studies have shown low-dose tam to have similar efficacy to standard dosing, with lower risk for adverse events. In this study, we aimed to evaluate uptake, adherence, and tolerability of low-dose tam in women at increased risk for breast cancer and those with ductal carcinoma in situ (DCIS). In this two-site prospective study, women who qualified for breast cancer RRM were offered participation and received consultation with a breast specialist for discussion of RRM rationale, benefits, side effects, and risks. Patients received baseline and 1-year follow-up surveys. A total of 41 patients consented for participation, and 31 completed 1-year follow-up. After initial consultation, 90% (n = 37) reported good/complete understanding of breast cancer risk. Of patients included in 1-year follow-up, 5 had DCIS, 13 had high-risk intraepithelial lesion, and 13 qualified based on Breast Cancer Risk Assessment Tool/International Breast Intervention Study calculation. Furthermore, 74% (n = 23) of patients reported that they took low-dose tam after consultation, with 78.2% (n = 18) of those still taking medication at 1 year. Patients who continued medication had higher estimated breast cancer risk compared with those who discontinued (International Breast Intervention Study 10-year risk, 12.7% vs. 7.6%; P = 0.027). All patients with DCIS initiated low-dose tam, and only one patient with DCIS had discontinued at 1 year. Uptake of low-dose tam after informed discussion is high. Adherence and tolerability at 1-year follow-up improved compared with those with traditional dosing of tam. Prevention Relevance: tam has been used extensively for breast cancer prevention in high-risk women. Historical uptake has been low because of concern for side effects and poor tolerability. Herein, we demonstrate that in the clinical setting, effective patient education and offering of a low-dose option can improve uptake in this high-risk population. See related Spotlight, p. 545.
MoreTranslated text
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
Related Papers
2011
被引用107 | 浏览
2003
被引用216 | 浏览
2015
被引用442 | 浏览
2010
被引用661 | 浏览
2016
被引用199 | 浏览
2018
被引用25 | 浏览
2016
被引用36 | 浏览
2019
被引用162 | 浏览
2019
被引用139 | 浏览
2021
被引用10 | 浏览
2023
被引用25 | 浏览
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
GPU is busy, summary generation fails
Rerequest