IRONMAN: A Novel International Registry of Men With Advanced Prostate Cancer
Bibliographic record
Abstract
PURPOSE: To describe a newly established international registry recruiting diverse patients with advanced prostate cancer across academic and community practices to address unmet needs in this population. PATIENTS AND METHODS: Initiated in 2017, IRONMAN (International Registry for Men with Advanced Prostate Cancer) is a prospective cohort of patients with advanced prostate cancer. The study will enroll 5,000 patients with metastatic hormone-sensitive prostate cancer (mHSPC) or castration-resistant prostate cancer (CRPC), recruited from Australia, the Bahamas, Barbados, Brazil, Canada, Ireland, Jamaica, Kenya, Nigeria, Norway, South Africa, Spain, Sweden, Switzerland, the United Kingdom, and the United States. The study is collecting datatypes to study variation in care and treatment of advanced prostate cancer across countries and across academic, community-based, and government practices with a focus on clinical outcomes, patient-reported outcomes, epidemiologic data, biologic subtypes, and clinician questionnaires. RESULTS: Through July 2022, 2,682 eligible patients were enrolled in 11 of 12 active countries. Sixty-six percent of patients have mHSPC, and 34% have CRPC. On the basis of self-report, 11% of patients are Black and 9% are Hispanic. Five Veterans Affairs Medical Centers are enrolling patients. Globally, 23% of patients report being veterans of military service. CONCLUSION: To our knowledge, this is the first international cohort of people newly diagnosed with advanced prostate cancer designed to describe variations in patient management, experiences, and outcomes. IRONMAN aims to identify optimal treatment sequences to improve survival, understand patient-reported outcomes, and explore novel biomarkers to understand treatment resistance mechanisms. Insights from IRONMAN will inform and guide future clinical management of people with mHSPC and CRPC. This cohort study will provide real-world evidence to facilitate a better understanding of the survivorship of people with advanced prostate cancer.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".