A patient-driven clinicogenomic partnership for metastatic prostate cancer
Bibliographic record
Abstract
Molecular profiling studies have enabled discoveries for metastatic prostate cancer (MPC) but have predominantly occurred in academic medical institutions and involved non-representative patient populations. We established the Metastatic Prostate Cancer Project (MPCproject, mpcproject.org), a patient-partnered initiative to involve patients with MPC living anywhere in the US and Canada in molecular research. Here, we present results from our partnership with the first 706 MPCproject participants. While 41% of patient partners live in rural, physician-shortage, or medically underserved areas, the MPCproject has not yet achieved racial diversity, a disparity that demands new initiatives detailed herein. Among molecular data from 333 patient partners (572 samples), exome sequencing of 63 tumor and 19 cell-free DNA (cfDNA) samples recapitulated known findings in MPC, while inexpensive ultra-low-coverage sequencing of 318 cfDNA samples revealed clinically relevant AR amplifications. This study illustrates the power of a growing, longitudinal partnership with patients to generate a more representative understanding of MPC.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".