cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.10592749
title: MedShapeNetCore

description: MedShapeNetCore is a subset of MedShapeNet, containing more lightweight 3D anatomical shapes in the format of mask, point cloud and mesh. The shape data are stored as numpy arrays in nested dictonaries in npz format (Zenodo). This API provides means to downloading, accessing and processing the shape data via Python, which integrates MedShapeNetCore seamless into Python-based machine learning workflows. For details, visit the Github Repository.  If you use the dataset in your research, please also cite the original datasets (which can be found using command <!python -m MedShapeNetCore info>), besides MedShapeNet.
DOI: 10.5281/zenodo.10623506
title: MedShapeNetCore

description: MedShapeNetCore is a subset of MedShapeNet, containing more lightweight 3D anatomical shapes in the format of mask, point cloud and mesh. The shape data are stored as numpy arrays in nested dictonaries in npz format (Zenodo). This API provides means to downloading, accessing and processing the shape data via Python, which integrates MedShapeNetCore seamless into Python-based machine learning workflows. For details, visit the Github Repository.  If you use the dataset in your research, please also cite the original datasets (which can be found using command <!python -m MedShapeNetCore info>), besides MedShapeNet.

 

Make sure to check out the MedShapeNetCore Show Case for the latest update.

 

@article{li2023medshapenet,
  title={MedShapeNet--A Large-Scale Dataset of 3D Medical Shapes for Computer Vision},
  author={Li, Jianning and Zhou, Zongwei and Yang, Jiancheng and others},
  journal={arXiv preprint arXiv:2308.16139},
  year={2023}
}

 

To install the MedShapeNetCore python package:

 pip install MedShapeNetCore

You can search the database using anatomy nomenclature, such as liver, aorta, skull, instrument etc.  Fore more commands, visit the Github Repository.

python -m MedShapeNetCore search_by_organ ORGAN

You can also directly download .stl files using the following command (replace ORGAN with the organ you want to search, e.g., liver, skull):

python -m MedShapeNetCore search_and_download ORGAN

Check the available .npz files:

python -m MedShapeNetCore info

Download .npz files to be used in python:

python -m MedShapeNetCore download FaceVRImport the python packages to load, visualize and process the .npz files:from MedShapeNetCore.MedShapeNetCore import MyDict,MSNLoader,MSNVisualizer,MSNSaver,MSNTransformer

Import shape dataloader:from MedShapeNetCore.MedShapeNetCore import BatchLoaderfacial_point=BatchLoader('facialVR','point',batch_size=2,shuffle=True)
for batch in facial_point:    print(batch.shape)

Use the fast load function to load a specified number of  samples  (2) of a given format ('point') from a dataset ('facialVR'):

msn_loader=MSNLoader()data=msn_loader.fast_load('facialVR','point',2,shuffle=True)
print(data.shape)
DOI: 10.6084/m9.figshare.25197005
title: Supplementary data: A US national update of health conditionprevalence among privately-insuredautistic adults

description: <b>This is a peer-reviewed supplementary table for the article '</b><b>A US national update of health condition </b><b>prevalence among privately-insured </b><b>autistic adults' published in the</b><b> </b><b><i>Journal of Comparative Effectiveness Research</i></b><b>.</b><b>Supplementary Table 1:</b> Study definitions of mental and physical health conditions using Healthcare Cost and Utilization Project (HCUP) Beta Multilevel Clinical Classification Software (CCS) or specific International Classification of Diseases, 10<sup>th</sup> edition (ICD-10) diagnosis codes.<b>Aim: </b>Previous research using state or regional samples has shown that autistic adults have a higherprevalence of health conditions in comparison to the general population. <b>Methods: </b>To build upon thisimportant previous research, we conducted a cross-sectional retrospective study of 2019–2020 healthcareclaims to determine the prevalence of conditions in a US national sample of privately insured autistic adults(n = 30,258) and an age- and sex-matched population comparison (n = 60,516) group of adults withoutautism diagnoses.<b> Results:</b> Like previous studies, we found that autistic adults had significantly greaterodds of most mental and physical health conditions. However, our prevalence estimates differed fromprevious studies for several mental and physical health conditions. For example, our sample of autisticadults had higher prevalence of anxiety disorders (55%) and attention deficit hyperactivity disorders(34%), but lower prevalence of asthma (9%) and sleep disorders (3%) than previous studies. <b>Discussion</b><b>& conclusion: </b>Our use of a large US national sample, more recent healthcare claims data, and differentmethods for identifying health conditions may have contributed to these differences. Our findings alerthealthcare providers and policymakers to the health conditions most common among the growingpopulation of autistic adults. We hope these findings lead to improved screening and management ofthese conditions, inform initiatives to improve access to healthcare, and guide future funding.
DOI: 10.5281/zenodo.10640334
title: pcap_file_team-6
DOI: 10.5281/zenodo.10640335
title: pcap_file_team-6
DOI: 10.5281/zenodo.10610936
title: Data for 10.1103/PhysRevA.109.012806

description: Data for the figures presented in "Hyperfine structure and isotope shifts of the (4s2)1S0→(4s4p)1P1 transition in atomic zinc"
DOI: 10.5281/zenodo.10610938
title: Data for 10.1103/PhysRevA.109.012806

description: Data for the figures presented in "Hyperfine structure and isotope shifts of the (4s2)1S0→(4s4p)1P1 transition in atomic zinc"
DOI: 10.5281/zenodo.10640886
title: ET_geoframe_Tier01
DOI: 10.5281/zenodo.10640887
title: ET_geoframe_Tier01
DOI: 10.6084/m9.figshare.25197023
title: Supplementary materials: Cost-utility of real-time continuous glucose monitoring versus self-monitoring of bloodglucose in people with insulin-treated Type II diabetes in France

description: <b>These are peer-reviewed supplementary tables for the article '</b><b>Cost-utility of real-time continuous glucose </b><b>monitoring versus self-monitoring of blood </b><b>glucose in people with insulin-treated Type </b><b>II diabetes in France</b><b>' published in the</b><b> </b><b><i>Journal of Comparative Effectiveness Research</i></b><b>.</b><br><b>Supplemental Table S1</b><b>:</b> Utility and disutility values for events/states.<b>Supplemental Table S2: </b>Costs per diabetes complication or event.<b>Supplemental Table S3: </b>Annual treatment and device costs.<b>Supplemental Table S4: </b>Projected diabetes complications for rt-CGM versus SMBG<b>References</b><b>:</b> References for online-only supplementary material.<b>Aim:</b> Clinical trials and real-world data for Type II diabetes both show that glycated hemoglobin (HbA1c)levels and hypoglycemia occurrence can be reduced by real-time continuous glucose monitoring (rt-CGM)versus self-monitoring of blood glucose (SMBG). The present cost-utility study investigated the long-termhealth economic outcomes associated with using rt-CGM versus SMBG in people with insulin-treated TypeII diabetes in France. <b>Materials & methods:</b> Effectiveness data were obtained from a real-world study,which showed rt-CGM reduced HbA1c by 0.56% (6.1 mmol/mol) versus sustained SMBG. Analyses wereconducted using the IQVIA Core Diabetes Model. A French payer perspective was adopted over a lifetimehorizon for a cohort aged 64.5 years with baseline HbA1c of 8.3% (67 mmol/mol). A willingness-to-paythreshold of €147,093 was used, and future costs and outcomes were discounted at 4% annually. <b>Results:</b>The analysis projected quality-adjusted life expectancy was 8.50 quality-adjusted life years (QALYs) for rt-CGM versus 8.03 QALYs for SMBG (difference: 0.47 QALYs), while total mean lifetime costs were €93,978for rt-CGM versus €82,834 for SMBG (difference: €11,144). This yielded an incremental cost-utility ratio(ICUR) of €23,772 per QALY gained for rt-CGM versus SMBG. Results were particularly sensitive to changesin the treatment effect (i.e., change in HbA1c), annual price and quality of life benefit associated withrt-CGM, SMBG frequency, baseline patient age and complication costs. <b>Conclusion:</b> The use of rt-CGM islikely to be cost-effective versus SMBG for people with insulin-treated Type II diabetes in France.
DOI: 10.6084/m9.figshare.25197023.v1
title: Supplementary materials: Cost-utility of real-time continuous glucose monitoring versus self-monitoring of bloodglucose in people with insulin-treated Type II diabetes in France

description: <b>These are peer-reviewed supplementary tables for the article '</b><b>Cost-utility of real-time continuous glucose </b><b>monitoring versus self-monitoring of blood </b><b>glucose in people with insulin-treated Type </b><b>II diabetes in France</b><b>' published in the</b><b> </b><b><i>Journal of Comparative Effectiveness Research</i></b><b>.</b><br><b>Supplemental Table S1</b><b>:</b> Utility and disutility values for events/states.<b>Supplemental Table S2: </b>Costs per diabetes complication or event.<b>Supplemental Table S3: </b>Annual treatment and device costs.<b>Supplemental Table S4: </b>Projected diabetes complications for rt-CGM versus SMBG<b>References</b><b>:</b> References for online-only supplementary material.<b>Aim:</b> Clinical trials and real-world data for Type II diabetes both show that glycated hemoglobin (HbA1c)levels and hypoglycemia occurrence can be reduced by real-time continuous glucose monitoring (rt-CGM)versus self-monitoring of blood glucose (SMBG). The present cost-utility study investigated the long-termhealth economic outcomes associated with using rt-CGM versus SMBG in people with insulin-treated TypeII diabetes in France. <b>Materials & methods:</b> Effectiveness data were obtained from a real-world study,which showed rt-CGM reduced HbA1c by 0.56% (6.1 mmol/mol) versus sustained SMBG. Analyses wereconducted using the IQVIA Core Diabetes Model. A French payer perspective was adopted over a lifetimehorizon for a cohort aged 64.5 years with baseline HbA1c of 8.3% (67 mmol/mol). A willingness-to-paythreshold of €147,093 was used, and future costs and outcomes were discounted at 4% annually. <b>Results:</b>The analysis projected quality-adjusted life expectancy was 8.50 quality-adjusted life years (QALYs) for rt-CGM versus 8.03 QALYs for SMBG (difference: 0.47 QALYs), while total mean lifetime costs were €93,978for rt-CGM versus €82,834 for SMBG (difference: €11,144). This yielded an incremental cost-utility ratio(ICUR) of €23,772 per QALY gained for rt-CGM versus SMBG. Results were particularly sensitive to changesin the treatment effect (i.e., change in HbA1c), annual price and quality of life benefit associated withrt-CGM, SMBG frequency, baseline patient age and complication costs. <b>Conclusion:</b> The use of rt-CGM islikely to be cost-effective versus SMBG for people with insulin-treated Type II diabetes in France.
DOI: 10.5281/zenodo.10491939
title: MRI Raw Data of Cardiac Radial Inversion Recovery FLASH Sequence (Part 1/9)

description: Other parts of the dataset are available at:



* Part 1: 10.5281/zenodo.10491940

* Part 2: 10.5281/zenodo.10492124

* Part 3: 10.5281/zenodo.10492202

* Part 4: 10.5281/zenodo.10492221

* Part 5: 10.5281/zenodo.10492234

* Part 6: 10.5281/zenodo.10492254

* Part 7: 10.5281/zenodo.10492268

* Part 8: 10.5281/zenodo.10492275

* Part 9: 10.5281/zenodo.10492300
DOI: 10.5281/zenodo.10491940
title: MRI Raw Data of Cardiac Radial Inversion Recovery FLASH Sequence (Part 1/9)

description: Other parts of the dataset are available at:



* Part 1: 10.5281/zenodo.10491940

* Part 2: 10.5281/zenodo.10492124

* Part 3: 10.5281/zenodo.10492202

* Part 4: 10.5281/zenodo.10492221

* Part 5: 10.5281/zenodo.10492234

* Part 6: 10.5281/zenodo.10492254

* Part 7: 10.5281/zenodo.10492268

* Part 8: 10.5281/zenodo.10492275

* Part 9: 10.5281/zenodo.10492300
DOI: 10.21227/5q9w-5t53
title: Synthetic Dataset for Induction Motor Broken Rotor Bar Analysis

description: The synthetic dataset was developed in the Power Electronics and Electrical Drive Laboratory (Laboratório de Eletrônica de Potência e Acionamento Elétrico – LEPAC, in portuguese) at the Federal University of Espírito Santo (UFES), Brazil. The dataset was generated from a computer simulation of a three-phase squirrel cage induction motor model with the insertion of faults in the rotor, specifically broken bars. The induction motor mathematical model with broken bar fault is based on reference [1].Real data from 26 induction motors are used as input parameters the adopted computational model, making it possible to generate a dataset containing 390 different simulated scenarios. For each induction motor, 5 fault scenarios are simulated, varying the number of broken bars between 0 (healthy rotor) and 4 broken bars. Furthermore, each scenario was simulated under 3 load levels (50%, 75% and 100% of nominal torque). The data from the 26 induction motors is available in a PDF provided together with the dataset in the compressed file. The files are saved in .mat format with the following nomenclature (MX.rYb.torqueZ.mat) where X refers to the induction motor identification (X = 1:26), Y refers to the number of broken bars (Y = 0:4) and Z indicates the percentage loading of the induction motor (Z= 50, 75, 100). Therefore, the nomenclature M3.r2b.torque50.mat, for example, corresponds to the data of induction motor 3 with 2 broken bars and 50% of nominal load torque. All simulations were carried out according to the following methodology. The induction motor is started with no load until it reaches steady state and, subsequently, the load torque for each simulation is set. In all scenarios for all induction motors, the steady state with load is reached after 50 seconds of simulation. The data saved in the .mat files are the three-phase motor currents and the simulation time step at a sampled frequency of 8kHz.
DOI: 10.5281/zenodo.10492123
title: MRI Raw Data of Cardiac Radial Inversion Recovery FLASH Sequence (Part 2/9)

description: Other parts of the dataset are available at:



* Part 1: 10.5281/zenodo.10491940

* Part 2: 10.5281/zenodo.10492124

* Part 3: 10.5281/zenodo.10492202

* Part 4: 10.5281/zenodo.10492221

* Part 5: 10.5281/zenodo.10492234

* Part 6: 10.5281/zenodo.10492254

* Part 7: 10.5281/zenodo.10492268

* Part 8: 10.5281/zenodo.10492275

* Part 9: 10.5281/zenodo.10492300
DOI: 10.5281/zenodo.10492124
title: MRI Raw Data of Cardiac Radial Inversion Recovery FLASH Sequence (Part 2/9)

description: Other parts of the dataset are available at:



* Part 1: 10.5281/zenodo.10491940

* Part 2: 10.5281/zenodo.10492124

* Part 3: 10.5281/zenodo.10492202

* Part 4: 10.5281/zenodo.10492221

* Part 5: 10.5281/zenodo.10492234

* Part 6: 10.5281/zenodo.10492254

* Part 7: 10.5281/zenodo.10492268

* Part 8: 10.5281/zenodo.10492275

* Part 9: 10.5281/zenodo.10492300
DOI: 10.5281/zenodo.10492253
title: MRI Raw Data of Cardiac Radial Inversion Recovery FLASH Sequence (Part 6/9)

description: Other parts of the dataset are available at:



* Part 1: 10.5281/zenodo.10491940

* Part 2: 10.5281/zenodo.10492124

* Part 3: 10.5281/zenodo.10492202

* Part 4: 10.5281/zenodo.10492221

* Part 5: 10.5281/zenodo.10492234

* Part 6: 10.5281/zenodo.10492254

* Part 7: 10.5281/zenodo.10492268

* Part 8: 10.5281/zenodo.10492275

* Part 9: 10.5281/zenodo.10492300
DOI: 10.5281/zenodo.10492254
title: MRI Raw Data of Cardiac Radial Inversion Recovery FLASH Sequence (Part 6/9)

description: Other parts of the dataset are available at:



* Part 1: 10.5281/zenodo.10491940

* Part 2: 10.5281/zenodo.10492124

* Part 3: 10.5281/zenodo.10492202

* Part 4: 10.5281/zenodo.10492221

* Part 5: 10.5281/zenodo.10492234

* Part 6: 10.5281/zenodo.10492254

* Part 7: 10.5281/zenodo.10492268

* Part 8: 10.5281/zenodo.10492275

* Part 9: 10.5281/zenodo.10492300
DOI: 10.5281/zenodo.10247043
title: Mycobacterium tuberculosis-specific antibodies detect progression to active tuberculosis in South African adolescents
DOI: 10.5281/zenodo.10247044
title: Mycobacterium tuberculosis-specific antibodies detect progression to active tuberculosis in South African adolescents
DOI: 10.5281/zenodo.10492201
title: MRI Raw Data of Cardiac Radial Inversion Recovery FLASH Sequence (Part 3/9)

description: Other parts of the dataset are available at:



* Part 1: 10.5281/zenodo.10491940

* Part 2: 10.5281/zenodo.10492124

* Part 3: 10.5281/zenodo.10492202

* Part 4: 10.5281/zenodo.10492221

* Part 5: 10.5281/zenodo.10492234

* Part 6: 10.5281/zenodo.10492254

* Part 7: 10.5281/zenodo.10492268

* Part 8: 10.5281/zenodo.10492275

* Part 9: 10.5281/zenodo.10492300
DOI: 10.5281/zenodo.10492202
title: MRI Raw Data of Cardiac Radial Inversion Recovery FLASH Sequence (Part 3/9)

description: Other parts of the dataset are available at:



* Part 1: 10.5281/zenodo.10491940

* Part 2: 10.5281/zenodo.10492124

* Part 3: 10.5281/zenodo.10492202

* Part 4: 10.5281/zenodo.10492221

* Part 5: 10.5281/zenodo.10492234

* Part 6: 10.5281/zenodo.10492254

* Part 7: 10.5281/zenodo.10492268

* Part 8: 10.5281/zenodo.10492275

* Part 9: 10.5281/zenodo.10492300
DOI: 10.5281/zenodo.10492220
title: MRI Raw Data of Cardiac Radial Inversion Recovery FLASH Sequence (Part 4/9)

description: Other parts of the dataset are available at:



* Part 1: 10.5281/zenodo.10491940

* Part 2: 10.5281/zenodo.10492124

* Part 3: 10.5281/zenodo.10492202

* Part 4: 10.5281/zenodo.10492221

* Part 5: 10.5281/zenodo.10492234

* Part 6: 10.5281/zenodo.10492254

* Part 7: 10.5281/zenodo.10492268

* Part 8: 10.5281/zenodo.10492275

* Part 9: 10.5281/zenodo.10492300
DOI: 10.5281/zenodo.10492221
title: MRI Raw Data of Cardiac Radial Inversion Recovery FLASH Sequence (Part 4/9)

description: Other parts of the dataset are available at:



* Part 1: 10.5281/zenodo.10491940

* Part 2: 10.5281/zenodo.10492124

* Part 3: 10.5281/zenodo.10492202

* Part 4: 10.5281/zenodo.10492221

* Part 5: 10.5281/zenodo.10492234

* Part 6: 10.5281/zenodo.10492254

* Part 7: 10.5281/zenodo.10492268

* Part 8: 10.5281/zenodo.10492275

* Part 9: 10.5281/zenodo.10492300
DOI: 10.5281/zenodo.10493094
title: MRI Raw Data of Cardiac Radial Real-Time BSSFP Sequence (Part 5/5)

description: Other parts of this dataset are available at:



* Part 1: 10.5281/zenodo.10492333

* Part 2: 10.5281/zenodo.10492337

* Part 3: 10.5281/zenodo.10492343

* Part 4: 10.5281/zenodo.10492455

* Part 5: 10.5281/zenodo.10493095
DOI: 10.5281/zenodo.10493095
title: MRI Raw Data of Cardiac Radial Real-Time BSSFP Sequence (Part 5/5)

description: Other parts of this dataset are available at:



* Part 1: 10.5281/zenodo.10492333

* Part 2: 10.5281/zenodo.10492337

* Part 3: 10.5281/zenodo.10492343

* Part 4: 10.5281/zenodo.10492455

* Part 5: 10.5281/zenodo.10493095
DOI: 10.5281/zenodo.10492274
title: MRI Raw Data of Cardiac Radial Inversion Recovery FLASH Sequence (Part 8/9)

description: Other parts of the dataset are available at:



* Part 1: 10.5281/zenodo.10491940

* Part 2: 10.5281/zenodo.10492124

* Part 3: 10.5281/zenodo.10492202

* Part 4: 10.5281/zenodo.10492221

* Part 5: 10.5281/zenodo.10492234

* Part 6: 10.5281/zenodo.10492254

* Part 7: 10.5281/zenodo.10492268

* Part 8: 10.5281/zenodo.10492275

* Part 9: 10.5281/zenodo.10492300
DOI: 10.5281/zenodo.10492275
title: MRI Raw Data of Cardiac Radial Inversion Recovery FLASH Sequence (Part 8/9)

description: Other parts of the dataset are available at:



* Part 1: 10.5281/zenodo.10491940

* Part 2: 10.5281/zenodo.10492124

* Part 3: 10.5281/zenodo.10492202

* Part 4: 10.5281/zenodo.10492221

* Part 5: 10.5281/zenodo.10492234

* Part 6: 10.5281/zenodo.10492254

* Part 7: 10.5281/zenodo.10492268

* Part 8: 10.5281/zenodo.10492275

* Part 9: 10.5281/zenodo.10492300
DOI: 10.5281/zenodo.10492233
title: MRI Raw Data of Cardiac Radial Inversion Recovery FLASH Sequence (Part 5/9)

description: Other parts of the dataset are available at:



* Part 1: 10.5281/zenodo.10491940

* Part 2: 10.5281/zenodo.10492124

* Part 3: 10.5281/zenodo.10492202

* Part 4: 10.5281/zenodo.10492221

* Part 5: 10.5281/zenodo.10492234

* Part 6: 10.5281/zenodo.10492254

* Part 7: 10.5281/zenodo.10492268

* Part 8: 10.5281/zenodo.10492275

* Part 9: 10.5281/zenodo.10492300
DOI: 10.5281/zenodo.10492234
title: MRI Raw Data of Cardiac Radial Inversion Recovery FLASH Sequence (Part 5/9)

description: Other parts of the dataset are available at:



* Part 1: 10.5281/zenodo.10491940

* Part 2: 10.5281/zenodo.10492124

* Part 3: 10.5281/zenodo.10492202

* Part 4: 10.5281/zenodo.10492221

* Part 5: 10.5281/zenodo.10492234

* Part 6: 10.5281/zenodo.10492254

* Part 7: 10.5281/zenodo.10492268

* Part 8: 10.5281/zenodo.10492275

* Part 9: 10.5281/zenodo.10492300
DOI: 10.6084/m9.figshare.25197044
title: Code Availability for Nature Communications Non-Hermitian Non-Equipartition Theory for Trapped Particles

description: Code Availability for Nature Communications Non-Hermitian Non-Equipartition Theory for Trapped Particles
DOI: 10.5281/zenodo.10640702
title: iperf_team3
DOI: 10.5281/zenodo.10640703
title: iperf_team3
DOI: 10.6084/m9.figshare.25197065.v1
title: Supplementary materials: Cost–effectiveness of switching from tenofovir disoproxil fumarate to tenofovir alafenamide versus entecavir for chronic hepatitis B patients in Greece

description: <b>These are peer-reviewed supplementary figures and tables for the article '</b><b>Cost–effectiveness of switching from </b><b>tenofovir disoproxil fumarate to tenofovir </b><b>alafenamide versus entecavir for chronic </b><b>hepatitis B patients in Greece</b><b>' published in the</b><b> </b><b><i>Journal of Comparative Effectiveness Research</i></b><b>.</b><br><b>Figure S1:</b> Model overview<b>Figure S2: </b>One-way sensitivity analyses QALYs (left) and costs (right), base case<b>Table S1: </b>Probability of HBV DNA levels after 48 weeks on treatment (copies/ml)<b>Table S2: </b>Probability of ALT level after 48 weeks on treatment (IU/ml)<b>Table S3: </b>Probability of HbeAg seroconversion after 48 weeks on treatment<b>Table S</b><b>4</b><b>: </b>HCC Risk Hazard Ratio by ALT Normalization<b>Table S</b><b>5</b><b>:</b> Probability of Achieving ALT Normalization<b>Aim:</b> This study assessed the clinical impact and cost–effectiveness of switching from tenofovir disoproxil fumarate (TDF) to either tenofovir alafenamide (TAF) or entecavir (ETV) in a Greek chronic hepatitis B (CHB) population. <b>Patients & methods: </b>A Markov model from the perspective of a third-party payer in Greece quantified the health and economic benefits of switching from TDF to either TAF or ETV over a lifetime horizon. <b>Results:</b> Over a lifetime, patients who switch from TDF to TAF versus patients who switch from TDF to ETV had an overall lower incidence of compensated cirrhosis (0.4% lower), decompensated cirrhosis (0.04% lower) and hepatocellular carcinoma (0.25% lower). Chronic kidney disease and endstage renal disease were also lower in patients who switch to TAF; major osteoporotic fractures were similar for both groups. While total costs were higher for switching from TDF to TAF versus TDF to ETV due to the higher cost of TAF, switching from TDF to TAF versus ETV was cost effective with an incremental cost–effectiveness ratio of €17,113 per quality-adjusted life year. Conclusion: Switching from TDF to TAF in patients living with CHB is a cost effective strategy to reduce adverse liver disease outcomes, while improving bone- and renal-related safety outcomes.