Patterns of care for renal surgery: Underutilization of nephron-sparing procedures
Bibliographic record
Abstract
Background: Nephron-sparing procedures are well-described, provide similar oncologic outcomes to nephrectomy, and potentially decrease morbidity as compared to nephrectomy.Methods: We analyzed academic and community health system data from Virginia and Kentucky to evaluate the utilization and cost of nephron-sparing procedures.Primary International Classification of Disease (ICD-9) diagnosis and procedure codes were employed to target subjects of interest.Results: In total, we analyzed 3809 subjects from Virginia and 3163 subjects from Kentucky between 2004 and 2009 who underwent treatment of a malignant renal mass.There has been a 6.1% and 14.8% decrease in nephrectomy utilization in Virginia and Kentucky, respectively, since 2004.In 2009, 71.4% and 68.8% of all procedures for the treatment of renal masses were radical nephrectomies.The proportion of nephron-sparing procedures has increased in academic (20%) and community (15%) health systems since 2004.The difference in cost between nephrectomy, partial nephrectomy and ablative therapy in Virginia and Kentucky hospitals was negligible (p > 0.05).Conclusions: Nephron-sparing procedures have been increasingly employed over the last 6 years, but are still underutilized.There does not appear to be a significant cost difference in the treatment of renal masses with nephrectomy, partial nephrectomy or ablative therapies.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".