A comparative population‐based analysis of the rate of partial vs radical nephrectomy for clinically localized renal cell carcinoma
Bibliographic record
Abstract
STUDY TYPE: Prevalence (prospective cohort with good follow up). LEVEL OF EVIDENCE: 1a. OBJECTIVE: To examine contemporary (1989-2004) trends in partial nephrectomy (PN) within the Surveillance, Epidemiology and End Results (SEER) database, as among other considerations, a survival benefit due to avoidance of surgically induced renal insufficiency distinguishes PN from radical nephrectomy (RN). PATIENTS AND METHODS: Diagnostic, stage and surgical codes of patients with T1-2N0M0 renal cell carcinoma treated with either PN or RN were assessed. Proportions, trends and multivariable logistic regression models tested the predictors of the use of PN. RESULTS: Of 19 733 assessable patients, 2614 (13.2%) and 17 119 (86.8%), respectively, had PN or RN. The use of PN decreased with increasing tumour size, was more frequent in younger patients and increased with more contemporary years of surgery (all P < 0.001). Intriguingly, there was important geographical variability (P < 0.001), e.g. in the San Francisco-Oakland Metropolitan Area the absolute PN rate was 16.4%, vs 7.6% in New Mexico (P < 0.001). In multivariable analyses, tumour size, age, year of surgery, gender and SEER registries were independent predictors of PN use. CONCLUSION: Although as expected the rate of PN use increased over time, unexplained variability remained. For example, gender and SEER registries affected the likelihood of PN. These variables warrant further analyses to reduce unnecessary variability and to maximize PN use and its benefit.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".