Defining ‘progression’ and triggers for curative intervention during active surveillance
Bibliographic record
Abstract
PURPOSE OF REVIEW: Low-risk and many cases of low-intermediate risk prostate cancer have little or no metastatic potential, and do not pose a threat to the patient in his lifetime. Substantial recent evidence, reviewed in this article, has clarified who these patients are and supports the use of conservative management in such individuals. RECENT FINDINGS: A key element of conservative management is the early identification of those 'low-risk' patients who harbour higher risk disease and benefit from definitive therapy. This represents about 30% of newly diagnosed low-risk patients. A further small proportion of patients with low-risk disease demonstrates true biological progression over time to higher grade disease (as distinct from grade increase on repeat biopsy due to resampling). Men with lower risk disease can defer treatment, in most cases for life. The results of active surveillance, embodying conservative management with selective delayed intervention for the subset who are reclassified as higher risk over time based on repeat biopsy, imaging or biomarker results, are associated with a 5% cancer-specific mortality at 15 years. SUMMARY: Active surveillance for low-risk prostate cancer is well tolerated in the intermediate-long term time frame. Further refinement of the surveillance approach is ongoing, incorporating MRI, targeted biopsies and molecular biomarkers to improve appropriate patient selection and triggers for intervention.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".