Real-World Validation of PinPoint Blood Tests in the NHS: Multivariable Machine Learning to Predict Cancer Risk in Primary Care Urgent Referrals
Matt Neal, Mark Dean, Sean Duffy, Roisean E. Ferguson, Peter Horan, Colin Johnston, Katherine L. Lloyd, Deehan Mair, Richard Neal, Jeanette Riley, Helen Ryan, Nigel Sansom, Richard S. Savage, Nisha Sharma, Jim Skinner, Giles Tully, Michelle Wilson, Will Worboys · 2026
Read Real-World Validation of PinPoint Blood Tests in the NHS: Multivariable Machine Learning to Predict Cancer Risk in Primary Care Urgent Referrals by Matt Neal, Mark Dean, Sean Duffy, Roisean E. Ferguson, Peter Horan, Colin Johnston, Katherine L. Lloyd, Deehan Mair, Richard Neal, Jeanette Riley, Helen Ryan, Nigel Sansom, Richard S. Savage, Nisha Sharma, Jim Skinner, Giles Tully, Michelle Wilson, Will Worboys (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/PMC13325911.txt.
Description
Matt Neal and colleagues validate machine learning blood tests for cancer risk in urgent NHS referrals in a 2026 service evaluation. General practitioners send over 3 million such referrals a year in England, yet only about 6 percent prove to be cancer.
The PinPoint set of nine blood tests ran across five hospital trusts and 170 practices from December 2020 to July 2025, covering 16,481 patients. Five tests looked clinically useful, led by the upper gastrointestinal test with an area under the curve of 0.86 in the results. Focusing on the top tenth by risk cut the number needed to investigate per cancer found by 2.6 to 6.1 fold.
Catalog record
| CREATOR | Matt Neal, Mark Dean, Sean Duffy, Roisean E. Ferguson, Peter Horan, Colin Johnston, Katherine L. Lloyd, Deehan Mair, Richard Neal, Jeanette Riley, Helen Ryan, Nigel Sansom, Richard S. Savage, Nisha Sharma, Jim Skinner, Giles Tully, Michelle Wilson, Will Worboys |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 41194 |
| LICENSE | https://creativecommons.org/licenses/by/4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |