Partial and radical nephrectomy provide comparable long‐term cancer control for <scp>T</scp>1b renal cell carcinoma
Bibliographic record
Abstract
OBJECTIVES: To examine utilization rates of partial nephrectomy relative to radical nephrectomy for T1b renal cell carcinoma in contemporary years, to identify sociodemographic and disease characteristics associated with partial nephrectomy use, and to compare effectiveness of partial versus radical nephrectomy with respect to cancer control. METHODS: Using the Surveillance, Epidemiology, and End Results database, 16,333 patients treated with partial or radical nephrectomy for T1bN0M0 renal cell carcinoma between 1988 and 2008 were identified. Logistic regression models were carried out to identify determinants of partial nephrectomy. Subsequently, cumulative incidence rates of cancer-specific and other-cause mortality between partial and radical nephrectomy were assessed, within the matched cohort. Furthermore, competing-risks regression analyses were used for prediction of cancer-specific mortality, after adjusting for other-cause mortality, and vice versa. RESULTS: The utilization rate of partial nephrectomy increased from 1.2% in 1988 to 15.9% in 2008 (P < 0.001). Younger individuals, smaller tumors, persons of black race, as well as men, were more likely to be treated with partial nephrectomy in the current cohort (all P ≤ 0.002). In the post-propensity cohort, the 5- and 10-year cancer-specific mortality rates were 4.4 and 6.1% for partial versus 6.0 and 10.4% for radical nephrectomy, respectively (P = 0.03). Competing-risks regression analyses showed that nephrectomy type was not statistically significantly associated with cancer-specific mortality, even after adjusting for other-cause mortality (hazard ratio 0.89, P = 0.5). CONCLUSIONS: Despite providing a comparable cancer control, the use of partial over radical nephrectomy for T1b renal cell carcinoma in USA has remained limited in recent years.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".