社会思想史研究と「憲法問題」にかんする覚書

departmental bulletin pape

Phenomenal Data

Article信州大学農学部演習林報告 18: 51-57(1981)departmental bulletin pape

Observation of the Color-Suppressed Decay B̅ 0→D0π0

journal articl

研究速報 : Fracture Behavior of a Completely Brittle Crack in Consideration of Restraining Stress between Atomic Planes

departmental bulletin pape

<教育デザインフォーラム学生発表会>高等学校における学習低成績生徒に対する学習フィードバックの効果について― Universal Design for Learning に基づく検討 ―(ポスター発表の要旨)

departmental bulletin pape

pungens

Arctostaphylos pungens  KunthMexican manzanita,  Pointleaf manzanitaKeene Camp Summit, Pines to Palms Hwy., San Jacinto Mts.Pine forest4900 feetPineShrub 6-8 feet tall, no burl formed but extensively layering and several-to-many stemmed from base

MOESM1 of Prediction and clinical utility of a contralateral breast cancer risk model

Additional file 1 Table S1. Data source flowchart. Table S2. Description of the studies included in the analyses. Table S3. Patients and first primary breast cancer characteristics used in the contralateral breast cancer risk prediction model in the complete case and all case analyses. Table S4. Results of multivariable subdistributional hazard model using the complete case dataset. Table S5. List of BCAC studies (including ABCS source) with the corresponding country and geographic area. Table S6. Main patient and disease characteristics. Table S7. Clinical utility of the 5-year contralateral breast cancer risk prediction model. Table S8. Results of multivariable subdistributional hazard model for breast cancer patients without BRCA mutations. Table S9. Clinical utility of the 5-year contralateral breast cancer risk prediction model in non-BRCA tested patients. Table S10. Clinical utility of the 10-year contralateral breast cancer risk prediction model in non-BRCA tested patients

MOESM2 of Prediction and clinical utility of a contralateral breast cancer risk model

Additional file 2 Figure S1. Graphical assessment of non-linear relationship of age with contralateral breast cancer risk. Figure S2. Visual assessment of calibration through calibration plots in the internal-external cross-validation at 5 years for the contralateral breast cancer risk model with BRCA mutation information. Figure S3. Visual assessment of calibration through calibration plots in the internal-external cross-validation at 10 years for the contralateral breast cancer risk model with BRCA mutation information. Figure S4. Decision curve analysis at 5 years for the contralateral breast cancer risk model including BRCA1/2 mutation information. Figure S5. Results of the leave-one-study-out cross-validation for the contralateral breast cancer risk model at 5 and 10 years without BRCA mutation information. Figure S6. Visual assessment of calibration through calibration plots in the internal-external cross-validation at 5 years for the contralateral breast cancer risk model without BRCA mutation information. Figure S7. Visual assessment of calibration through calibration plots in the internal-external cross-validation at 10 years for the contralateral breast cancer risk model without BRCA mutation information. Figure S8. Density distribution of 10-year predicted absolute risk in patients with no family history and patients with a family history. Figure S9. Decision curve analysis at 5 years for the contralateral breast cancer risk model without BRCA mutation information. Figure S10. Decision curve analysis at 10 years for the contralateral breast cancer risk model without BRCA mutation information. Figure S11. Assessment of inclusion of information of contralateral preventive mastectomy (CPM)

MOESM3 of Prediction and clinical utility of a contralateral breast cancer risk model

Additional file 3. Supplementary methods

Additional file 4 of Common variants in breast cancer risk loci predispose to distinct tumor subtypes

Additional file 4. Funding and Acknowledgement. This file contains the additional funding not included in the main text, the acknowledgments, and the names of the people in the collaboration groups