Assessment of local tumor ablation and non-interventional management versus partial nephrectomy in T1a renal cell carcinoma
Bibliographic record
Abstract
BACKGROUND: Local tumor ablation (LTA) and non-interventional management (NIM) emerged as alternative management options for T1a renal cell carcinoma (RCC). We investigated trends and cancer-specific mortality (CSM) after LTA and NIM, compared to partial nephrectomy (PN). METHODS: Within the Surveillance, Epidemiology, and End Results database (2004-2015), T1a RCC patients treated with PN, LTA or NIM were identified. Estimated annual proportion change methodology (EAPC), 1:1 ratio propensity score (PS) matching, cumulative incidence plots and multivariable competing risks regression models (CRR) were used to compare LTA vs. PN and NIM vs. PN. Subgroup analyses focused on patients <65 and ≥65 years. RESULTS: Overall 4524 patients underwent LTA vs. 1654 NIM vs. 25,435 PN. Annuals rates increased for NIM (EAPC: +3.3%, P<0.001), but not for either LTA or PN. After PS-matching in multivariable CCR, LTA (HR 1.9, P<0.001) and NIM (HR 3.0, P<0.001) showed worse 5-year CSM, relative to PN. In subgroup analyses, LTA showed no CSM disadvantage relative to PN in younger patients (HR 2.0, P=0.07). In older patients 1.64-fold CSM increase was recorded. Conversely, NIM younger (HR 3.1, P=0.001) and older (HR 3.1, P<0.001) patients exhibited higher CSM relative to PN. CONCLUSIONS: In T1a RCC patients, NIM rates showed a modest but significant increase, while LTA and PN rates remained stable. In survival analyses, LTA exhibited higher CSM rates only for elderly patients. Conversely, NIM exhibited higher CSM rates in both younger and older patients.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".