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Randomized Trial of Fetal Surgery for Moderate Left Diaphragmatic Hernia

The New England journal of medicine, 2021-07, Vol.385 (2), p.119-129

2021

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Explaining Support Vector Machines: A Color Based Nomogram

PloS one, 2016-10, Vol.11 (10), p.e0164568-e0164568

2016

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Pregnancy of unknown location: a consensus statement of nomenclature, definitions, and outcome

Fertility and sterility, 2011-03, Vol.95 (3), p.857-866

2011

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Long-term cognitive and cardiac outcomes after prenatal exposure to chemotherapy in children aged 18 months or older: an observational study

The lancet oncology, 2012-03, Vol.13 (3), p.256-264

2012

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A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models

Journal of clinical epidemiology, 2019-06, Vol.110, p.12-22

2019

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The harm of class imbalance corrections for risk prediction models: illustration and simulation using logistic regression

Journal of the American Medical Informatics Association : JAMIA, 2022-08, Vol.29 (9), p.1525-1534

2022

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Net benefit approaches to the evaluation of prediction models, molecular markers, and diagnostic tests

BMJ (Online), 2016-01, Vol.352, p.i6

2016

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Letter to the editor: The use of fast approximate random forests using subsampling (rfsrc.fast) does not support the conclusion on sample sizes needed for random survival forests

Statistics in medicine, 2024-10, Vol.43 (24), p.4778-4780

2024

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A calibration hierarchy for risk models was defined: from utopia to empirical data

Journal of clinical epidemiology, 2016-06, Vol.74, p.167-176

2016

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Reporting and Interpreting Decision Curve Analysis: A Guide for Investigators

European urology, 2018-12, Vol.74 (6), p.796-804

2018

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Calibration: the Achilles heel of predictive analytics

BMC medicine, 2019-12, Vol.17 (1), p.230-230, Article 230

2019

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Randomized Trial of Fetal Surgery for Severe Left Diaphragmatic Hernia

The New England journal of medicine, 2021-07, Vol.385 (2), p.107-118

2021

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Machine Learning in Medicine

The New England journal of medicine, 2019-06, Vol.380 (26), p.2588-2590

2019

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A note on estimating the Cox‐Snell R2 from a reported C statistic (AUROC) to inform sample size calculations for developing a prediction model with a binary outcome

Statistics in medicine, 2021-02, Vol.40 (4), p.859-864

2021

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The association of weather conditions with day-to-day variability in physical activity in patients with COPD

ERJ open research, 2023-11, Vol.9 (6), p.314

2023

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Posttraumatic stress, anxiety and depression following miscarriage and ectopic pregnancy: a multicenter, prospective, cohort study

American journal of obstetrics and gynecology, 2020-04, Vol.222 (4), p.367.e1-367.e22

2020

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Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence

BMJ open, 2021-07, Vol.11 (7), p.e048008-e048008

2021

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There is no such thing as a validated prediction model

BMC medicine, 2023-02, Vol.21 (1), p.70-8, Article 70

2023

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Regression shrinkage methods for clinical prediction models do not guarantee improved performance: Simulation study

Statistical methods in medical research, 2020-11, Vol.29 (11), p.3166-3178

2020

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Morcellation and risk of malignancy in presumed ovarian fibromas/fibrothecomas

The lancet oncology, 2016-03, Vol.17 (3), p.273-274

2016

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Poor performance of clinical prediction models: the harm of commonly applied methods

Journal of clinical epidemiology, 2018-06, Vol.98, p.133-143

2018

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ROC curves for clinical prediction models part 1. ROC plots showed no added value above the AUC when evaluating the performance of clinical prediction models

Journal of clinical epidemiology, 2020-10, Vol.126, p.207-216

2020

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Critical appraisal of artificial intelligence-based prediction models for cardiovascular disease

European heart journal, 2022-08, Vol.43 (31), p.2921-2930

2022

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Demystifying AI in healthcare

BMJ (Online), 2020-09, p.m3505

2020

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Randomized Trial of Fetal Surgery for Moderate Left Diaphragmatic Hernia

Explaining Support Vector Machines: A Color Based Nomogram

Pregnancy of unknown location: a consensus statement of nomenclature, definitions, and outcome

Long-term cognitive and cardiac outcomes after prenatal exposure to chemotherapy in children aged 18 months or older: an observational study

A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models

The harm of class imbalance corrections for risk prediction models: illustration and simulation using logistic regression

Net benefit approaches to the evaluation of prediction models, molecular markers, and diagnostic tests

Letter to the editor: The use of fast approximate random forests using subsampling (rfsrc.fast) does not support the conclusion on sample sizes needed for random survival forests

A calibration hierarchy for risk models was defined: from utopia to empirical data

Reporting and Interpreting Decision Curve Analysis: A Guide for Investigators

Calibration: the Achilles heel of predictive analytics

Randomized Trial of Fetal Surgery for Severe Left Diaphragmatic Hernia

Machine Learning in Medicine

A note on estimating the Cox‐Snell R2 from a reported C statistic (AUROC) to inform sample size calculations for developing a prediction model with a binary outcome

The association of weather conditions with day-to-day variability in physical activity in patients with COPD

Posttraumatic stress, anxiety and depression following miscarriage and ectopic pregnancy: a multicenter, prospective, cohort study

Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence

There is no such thing as a validated prediction model

Regression shrinkage methods for clinical prediction models do not guarantee improved performance: Simulation study

Morcellation and risk of malignancy in presumed ovarian fibromas/fibrothecomas

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

Poor performance of clinical prediction models: the harm of commonly applied methods

ROC curves for clinical prediction models part 1. ROC plots showed no added value above the AUC when evaluating the performance of clinical prediction models

Critical appraisal of artificial intelligence-based prediction models for cardiovascular disease

Demystifying AI in healthcare

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