Radical versus partial nephrectomy
Bibliographic record
Abstract
BACKGROUND: Relative to radical nephrectomy (RN), partial nephrectomy (PN) performed for renal cell carcinoma (RCC) may protect from non-cancer-related deaths. The authors tested this hypothesis in a cohort of PN and RN patients. METHODS: The Surveillance, Epidemiology, and End Results-9 database allowed identification of 2198 PN (22.4%) and 7611 RN (77.6%) patients treated for T1aN0M0 RCC between 1988 and 2004. Analyses matched for age, year of surgery, tumor size, and Fuhrman grade addressed the effect of nephrectomy type (RN vs PN) on overall mortality (Cox regression models) and on non-cancer-related mortality (competing-risks regression models). RESULTS: Relative to PN, RN was associated with 1.23-fold (P = .001) increased overall mortality rate, which translated into a 4.9% and 3.1% absolute increase in mortality at 5 and 10 years after surgery, respectively. Similarly, non-cancer-related death rate was significantly higher after RN in competing-risks regression models (P < .001), which translated into a 4.6% and 4.5% absolute increase in non-cancer-related mortality at 5 and 10 years after surgery, respectively. CONCLUSIONS: Relative to PN, RN predisposes to an increase in overall mortality and non-cancer-related death rate in patients with T1a RCC. In consequence, PN should be attempted whenever technically feasible. Selective referrals should be considered if PN expertise is unavailable.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.006 | 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".