Rates of open versus laparoscopic and partial versus radical nephrectomy for <scp>T</scp>1a renal cell carcinoma: A population‐based evaluation
Bibliographic record
Abstract
OBJECTIVES: To examine the trends of open and laparoscopic partial nephrectomy and radical nephrectomy according to sociodemographic and tumor characteristics. METHODS: Using the Surveillance, Epidemiology, and End Results Medicare-linked database, 6024 patients diagnosed with T1a renal cell carcinoma were abstracted. Multivariable logistic regression analyses were used for prediction of open radical nephrectomy, open partial nephrectomy, laparoscopic radical nephrectomy and laparoscopic partial nephrectomy. Covariates comprised of patient age, baseline comorbidity status, sex, race, marital status, socioeconomic status, population density, Surveillance, Epidemiology and End Results registry, tumor size, and year of diagnosis. RESULTS: Open radical nephrectomy decreased from 89% in 1988 to 66% in 2005 (P < 0.001), whereas open partial nephrectomy increased from 7% to 29% (P < 0.001). Meanwhile, utilization of either laparoscopic radical nephrectomy or laparoscopic partial nephrectomy remained low. Treatment utilization differed according to Surveillance, Epidemiology, and End Results registries (P < 0.001). Increasing patient age, female sex, low socioeconomic status and unmarried status (all P ≤ 0.003) were predictors of open radical nephrectomy. The utilization rates of laparoscopic radical nephrectomy or laparoscopic partial nephrectomy varied minimally according to the examined characteristics. Older patients or women were significantly more likely to undergo laparoscopic radical nephrectomy, even after adjustment for all covariates (both P ≤ 0.02). CONCLUSIONS: The rising utilization rates of radical nephrectomy are encouraging. Nevertheless, disparities of treatment type still exist. It is of concern that older and female patients are less likely to undergo nephron-sparing surgery, and to have a radical nephrectomy by the laparoscopic approach instead.
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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 | 0.001 |
| 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".