Irish Prostate Cancer Outcomes Research (IPCOR) registry: cohort profile
Bibliographic record
Abstract
PURPOSE: To describe the Irish Prostate Cancer Outcomes Research (IPCOR) registry. The cohort was collected to inform and improve the prostate cancer journey of men in Ireland. PARTICIPANTS: Established in 2015, IPCOR was a unique large-scale prospective cohort study registering men with prostate cancer in Ireland. From 2016 to 2020, IPCOR collected data on 6816 men who were newly diagnosed with prostate cancer across 16 hospitals, both public and private. A comprehensive clinical dataset was collected, capturing detailed information on men's diagnosis, treatments and follow-up. In addition, a subset of 873 men completed patient-reported outcome measures. FINDINGS TO DATE: The IPCOR study has revealed several key insights into prostate cancer diagnosis and treatment in Ireland. The data indicate a high rate of diagnosis through opportunistic Prostate-Specific Antigen screening, with many cases identified at an early stage. FUTURE PLANS: IPCOR invites collaboration from the global cancer research community to use this resource to advance prostate cancer research and improve patient outcomes worldwide. IPCOR aims to continue updating long-term survival follow-up data for this cohort. It also plans to continue its collaborative approach with patients, engaging with the Lived Experience Advisory Panel in interpreting results emerging from this dataset. Moving forward, IPCOR is planning its next phase of the project, IPCOR 2.0. This will be a prospective, longitudinal, multi-centre clinical quality registry and biorepository.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.007 |
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".