Prevalence of MRI lesions in men responding to a GP-led invitation for a prostate health check: a prospective cohort study
Bibliographic record
Abstract
Objective: In men with a raised prostate-specific antigen (PSA), MRI increases the detection of clinically significant cancer and reduces overdiagnosis, with fewer biopsies. MRI as a screening tool has not been assessed independently of PSA in a formal screening study. We report a systematic community-based assessment of the prevalence of prostate MRI lesions in an age-selected population. Methods and analysis: ) were recommended for standard National Health Service (NHS) prostate cancer assessment. Results: Eight GP practices sent invitations to 2096 men. 457 men (22%) responded and 303 completed both screening tests. Older white men were most likely to respond to the invitation, with black men having 20% of the acceptance rate of white men.One in six men (48/303 men, 16%) had a positive screening MRI, and an additional 1 in 20 men (16/303, 5%) had a raised PSA density alone. After NHS assessment, 29 men (9.6%) were diagnosed with clinically significant cancer and 3 men (1%) with clinically insignificant cancer.Two in three men with a positive MRI, and more than half of men with clinically significant disease had a PSA <3 ng/mL. Conclusions: Prostate MRI may have value in screening independently of PSA. These data will allow modelling of the use of MRI as a primary screening tool to inform larger prostate cancer screening studies. Trial registration number: NCT04063566.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".