Incidence of Local Treatment and Metastasis During Active Surveillance for Patients With a Small Renal Mass in a National Multicenter Prospective Cohort
Bibliographic record
Abstract
PURPOSE: The objective of this study was to determine the incidence of local treatment and incidence of metastasis for patients with a solitary small renal mass (SRM; ≤4 cm) initiating active surveillance (AS). MATERIALS AND METHODS: Patients enrolled in the Canadian Kidney Cancer information system between January 2011 and January 2023 with a solitary renal mass ≤ 4 cm opting for AS were included. The primary outcome was local treatment progression, achieved if the patient received definitive local treatment after initiating AS. The secondary outcomes were growth rate progression (>0.5 cm/y), size progression (>4 cm), composite progression (either size or growth rate progression), and development of metastases. RESULTS: The Canadian Kidney Cancer information system included 1393 patients who initiated AS for an SRM ≤ 4 cm during the study period. At a median follow-up of 4.0 years (95% CI 2.1-6.4), 238 patients received local treatment, and of these, 195 were nephron sparing. Two- and 5-year cumulative incidence of treatment was 8.4% (95% CI 6.9-10) and 21% (95% CI 19-24), respectively. Twenty-nine patients developed metastasis. Two- and 5-year cumulative incidence of metastasis was 0.67% (95% CI 0.32-1.3) and 2.3% (95% CI 1.5-3.5), respectively. Of the 29 patients who developed metastases, 23 had progressed using size or growth rate cutoffs, and 7 had received local treatment with curative intent before the identification of metastases. CONCLUSIONS: Patients choosing surveillance for an SRM have low cumulative incidence of local treatment and metastasis at 5 years, demonstrating AS is a safe initial management approach.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".