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Daniel Golden on Muck Rack

Daniel Golden

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Boston
As seen in: ProPublica, Apple Podcasts, Medium, MSN, Bloomberg News, Nature, The Guardian, The Wall Street Journal, CNBC, Gizmodo, Memory Studies and
ProPublica senior editor & reporter. Author, The Price of Admission and Spy Schools; co-author, The Ransomware Hunting Team. Religion: Red Sox-ism

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Daniel Golden’s Biography

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Daniel Golden is a senior editor at ProPublica, a non-profit website for investigative reporting. His latest book, Spy Schools: How The CIA, FBI, and Foreign Intelligence Secretly Exploit America's Universities, is scheduled to be published by Henry Holt in October 2017. Some annoyed college administrators have called him a muckraker, or a gadfly. Both are labels he wears with pride.

Before joining ProPublica in October 2016, Golden worked as managing editor for education and enterprise at Bloo…

Daniel Golden’s Journalist Portfolio

The Story Behind Jared Kushner's Curious Acceptance into Harvard

The Story Behind Jared Kushner's Curious Acceptance into Harvard

Description of how I was able to document in my book, "The Price of Admission," that Harvard admitted Jared K…

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Who's Taking College Spots From Top Asian Americans? Privileged Whites.

Who's Taking College Spots From Top Asian Americans? Privileged Whites.

The Trump administration is preparing to investigate whether Asian Americans are treated unfairly as a result…

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Sex, Lies and Espionage: Did a Professor Spy for the FBI?

Sex, Lies and Espionage: Did a Professor Spy for the FBI?

The FBI pressured University of South Florida Professor Dajin Peng to spy on China--and even asked USF to set…

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China's Test Prep Juggernaut

China's Test Prep Juggernaut

How a Chinese test-prep firm cracked the SAT code.

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Daniel Golden of The Wall Street Journal

Daniel Golden of The Wall Street Journal

My Pulitzer-winning articles on preferences for the rich in college admissions.

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Articles

Generalization of AI-Based Gestational Age Assessment Using Blind Sweep Ultrasonography

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By Justin D. Krogue, Anneka Wickramanayake, Nichole Young-Lin, Alicia Martin, Tiya Tiyasirichokchai, Akib Uddin, Yun Liu, Patricia H Strachan, Sunny Jansen, Preeti Singh Verified, Catherine Arguelles, Kenneth S. Hudson, David Melnick, Mozziyar Etemadi, Yossi Matias, Avinatan Hassidim, Greg Corrado, Shruthi Prabhakara, Daniel Golden Verified, Ryan G. Gomes, Shravya Shetty, Angelica Willis, Chace Lee, Stacey Caron, Priscah Cheruiyot, Amber Watters, Alyssa Santi, Mahesh Vaidyanathan
| JAMA Network Verified
Supplement 1. eMethods. Exploratory analysis eTable. Secondary and exploratory comparisons of model performance against clinical standard procedure (Hadlock formula) eFigure 2. Component interaction diagram of the study exam system eFigure 3. Distribution of participants per number of blind sweep examinations in Chicago Supplement 2. Figure 1. Flow Diagram of Patient-Level Data Flow for Primary Analysis (opens in new tab) GA indicates gestational age. Figure 2.
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Impact of using artificial intelligence as a second reader in breast screening including arbitration

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By Lucy Warren, Jenny Venton, Kenneth C Young, Mark Halling-Brown, Christopher Kelly, Marc Wilson, Deborah Cunningham, Richard Sidebottom, Mamatha M. Reddy, Hema Purushothaman, Lesley Honeyfield, Reena Chopra, Aminata Sy, Dominic Ward, Lin Yang, Rory Sayres, Daniel Golden Verified, Namrata Malhotra, Della Ogunleye, Charlotte Purdy, Alistair Mackenzie, Susan Thomas, Shravya Shetty, Fiona Gilbert, Ara Darzi, Hutan Ashrafian, Megumi Morigami, Lisanne Khoo, Delara Khodabakhshi, Amandeep Hujan, Tsvetina Stoycheva, Andy Joiner, Rachita Mallya, Lihong Xi
| Nature Verified
Abstract The impact of incorporating artificial intelligence (AI) into a double-read breast-screening workflow, including arbitration, is unclear. This retrospective study included 50,000 representative women from two NHS breast-screening centers. All the women had long-term follow-up, allowing us to determine whether use of AI leads to earlier cancer detection. Cases requiring arbitration (8,732 cases) were read by 22 readers in a reader study, following their normal arbitration workflow.
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Diagnostic accuracy, fairness and clinical implementation of AI for breast cancer screening: results of multicenter retrospective and prospective technical feasibility studies

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By Christopher Kelly, Marc Wilson, Lucy Warren, Richard Sidebottom, Mark Halling-Brown, Lin Yang, Jenny Venton, Reena Chopra, Jane Chang, Jonathan Dixon, Fiona Gilbert, Daniel Golden Verified, Lesley Honeyfield, Emma Lewis, Namrata Malhotra, Della Ogunleye, Charlotte Purdy, Rory Sayres, Marcin Sieniek, Aminata Sy, Susan Thomas, Dominic Ward, Shawn Xu, Shravya Shetty, Ara Darzi, Kenneth R. Young, Hema Purushothaman, Mamatha M. Reddy, Hutan Ashrafian, Deborah Cunningham, Megumi Morigami, Elzbieta Gruzewska, Amandeep Hujan, Delara Khodabakhshi, Rachita Mallya, Tsvetina Stoycheva, Lihong Xi, Lisanne Khoo
| Nature Verified
Abstract Artificial intelligence (AI) promises to enhance breast cancer screening. Here we evaluated Google’s mammography AI system (version 1.2) across two phases: a retrospective study using 115,973 mammograms from five National Health Service screening services with 39-month follow-up and prospective noninterventional feasibility deployment at 12 sites (9,266 cases). The primary endpoint was AI sensitivity and specificity versus first reader using a 5% noninferiority margin.
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Interview

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What was your first job as a journalist?

At the Amherst Record in Amherst, Mass.

Have you ever used a typewriter?

Yes

Who's your favorite fictional journalist?

Jack Burden in "All the King's Men"