When is prostate cancer really cancer?
Bibliographic record
Abstract
Prostate cancer (PC) is a major cause of cancer-related deaths worldwide, with far more diagnoses than deaths annually. Recent discussions have challenged whether Grade Group 1 (GG1) PC should be labeled "cancer" due to its indolent nature. To address this question, an international symposium convened stakeholders from various fields. We summarize key discussion points: autopsy studies reveal GG1 is so common in aging males as to be perhaps a normal aspect of aging. Pure GG1 has no capacity to metastasize. Modern diagnostic pathways focus on detecting higher-grade disease, explicitly omitting biopsy if GG 2 or higher is not suspected, so GG1 has effectively become an "incidentaloma." Recent spatial transcriptomics of prostate sections identifies a continuum of genomic changes-including alterations characteristic of malignancy in histologically normal regions, so the designation of cancer based entirely on conventional pathology findings increasingly seems arbitrary at least to an extent. Pathologists discussed heterogeneity and diagnostic challenges, suggesting "acinar neoplasm" as one possible alternative label. GG1 should not be considered "normal," and absolutely requires ongoing active surveillance; whether patients would adhere to surveillance absent a cancer diagnosis is unknown. Patient perspectives highlighted the adverse effects of overtreatment and the burden of a cancer diagnosis. The anticipated impact on screening and treatment varies across health-care systems, but many believe public health would on balance greatly improve if GG1-along with lesions in other organs with no capacity to cause symptoms or threaten life-were labeled something other than "cancer." Ultimately, our goal is to reduce PC mortality while minimizing harms associated with both overdiagnosis and overtreatment.
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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 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.009 | 0.020 |
| Insufficient payload (model declined to judge) | 0.006 | 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".