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Radiation enhancement using focussed ultrasound-stimulated microbubbles for breast cancer: A Phase 1 clinical trial

Radiation enhancement using focussed ultrasound-stimulated microbubbles for breast cancer: A Phase 1 clinical trial
plos.org - get the latest breaking news, showbiz & celebrity photos, sport news & rumours, viral videos and top stories from plos.org Daily Mail and Mail on Sunday newspapers.

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TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods

TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods
bmj.com - get the latest breaking news, showbiz & celebrity photos, sport news & rumours, viral videos and top stories from bmj.com Daily Mail and Mail on Sunday newspapers.

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Development and Validation of a Risk Score Predicting Death Without Transplant in Adult Heart Transplant Candidates | Surgery | JAMA

This study develops and validates a multivariable prediction model to rank order adult heart transplant candidates by medical urgency. ....

National Institutes Of Health , United States , Williamf Parker , University Of Chicago , Blood Institute , R Foundation For Statistical Computing , Us Department Of Health , Transplantation Network , Security Administration , National Institute , University Of Chicago Biological Sciences Division , Greenwall Foundation , National Institute Of Diabetes , Department Of Defense , Boehringer Ingelheim , National Heart , Human Services , Candidate Risk Score , French Candidate Risk Score , Organ Procurement , Risk Score , Study Population , Scientific Registry , Transplant Recipients , Transparent Reporting , Multivariable Prediction Model ,

AI models show promise in predicting heart disease risks, but lack validation

Review highlights the potential of AI models in predicting cardiovascular disease risks but emphasizes the need for independent external validation to ensure their clinical applicability. ....

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Evaluation of clinical prediction models (part 3): calculating the sample size required for an external validation study

An external validation study evaluates the performance of a prediction model in new data, but many of these studies are too small to provide reliable answers. In the third article of their series on model evaluation, Riley and colleagues describe how to calculate the sample size required for external validation studies, and propose to avoid rules of thumb by tailoring calculations to the model and setting at hand.

External validation studies evaluate the performance of one or more prediction models (eg, developed previously using statistical, machine learning, or artificial intelligence approaches) in a different dataset to that used in the model development process.1 2 3 Part 2 in our series describes how to undertake a high quality external validation study,4 including the need to estimate model performance measures such as calibration (agreement between observed and predicted values), discrimination (separation between predicted values in those with and without an outcome eve ....

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