Management of Localized Kidney Cancer: Calculating Cancer-specific Mortality and Competing Risks of Death for Surgery and Nonsurgical Management
Bibliographic record
Abstract
BACKGROUND: For elderly individuals with localized renal cell carcinoma (RCC), surgical intervention remains the primary treatment option but may not benefit patients with limited life expectancy. OBJECTIVE: To calculate the trade-offs between surgical excision and nonsurgical management (NSM) with respect to competing causes of mortality. DESIGN, SETTING, AND PARTICIPANTS: Relying on a cohort of Medicare beneficiaries, all patients with nonmetastatic node-negative T1 RCC between 1988 and 2005 were abstracted. INTERVENTION: All patients were treated with partial nephrectomy (PN), radical nephrectomy (RN), or NSM. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Cancer-specific mortality (CSM) and other-cause mortality (OCM) rates were modeled through competing-risks regression methodologies. Instrumental variable analysis was used to account for the potential biases associated with measured and unmeasured confounders. RESULTS AND LIMITATIONS: A total of 10 595 patients were identified. In instrumental variable analysis, patients treated with PN (hazard ratio [HR]: 0.45; 95% confidence interval [CI], 0.24-0.83; p=0.01) or RN (HR: 0.58; 95% CI, 0.35-0.96; p=0.03) had a significantly lower risk of CSM than those treated with NSM. In subanalyses restricted to patients ≥ 75 yr, the instrumental variable analysis failed to detect any statistically significant difference between PN (HR: 0.48; p=0.1) or RN (HR: 0.57; p=0.1) relative to NSM with respect to CSM. Similar trends were observed in T1a RCC only. CONCLUSIONS: PN or RN is associated with a reduction of CSM among older patients diagnosed with localized RCC, compared with NSM. The same benefit failed to reach statistical significance among patients ≥ 75 yr. The harms of surgery need to be weighed against the marginal survival benefit for some patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".