A population‐based comparison of survival after nephrectomy vs nonsurgical management for small renal masses
Bibliographic record
Abstract
OBJECTIVE To examine population‐based rates of cancer‐specific and other‐cause mortality after either non‐surgical management (NSM) or nephrectomy, in patients with small renal masses, as several reports from selected institutions support the applicability of surveillance in patients with small renal masses, but there are no population‐based studies confirming the general applicability of this therapy. PATIENTS AND METHODS Of 43 143 patients with renal cell carcinoma identified in the 1988–2004 Surveillance, Epidemiology and End Results database, 10 291 had localized small renal masses (≤4 cm) and were offered NSM (433, 4.2%) or nephrectomy (9858, 95.8%). Univariable matched and multivariable unmatched competing‐risks regression models were used in the analyses. RESULTS Cumulative incidence plots based on unmatched data, where the effect of other‐cause mortality was controlled for, showed a 5.2%, 6.5% and 9.4% survival benefit for nephrectomy vs NSM at 1, 2 and 5 years after nephrectomy or diagnosis, respectively. The same magnitude of the benefit (4.5%, 5.6% and 8.0%) persisted in analyses matched for age, tumour size and year of diagnosis or of nephrectomy. Finally, in multivariable analyses, treatment type, age, tumour size and year of diagnosis or of nephrectomy were independent predictors. CONCLUSION Relative to nephrectomy, NSM appears to undermine the overall and cancer‐specific survival of patients with small renal masses by as much as 9.4%, at 5 years.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".