Practice‐setting and surgeon characteristics heavily influence the decision to perform partial nephrectomy among <scp>A</scp> merican <scp>U</scp> rologic <scp>A</scp> ssociation surgeons
Bibliographic record
Abstract
UNLABELLED: WHAT'S KNOWN ON THE SUBJECT? AND WHAT DOES THE STUDY ADD?: There is great variability in the utilization of partial nephrectomy, but the causes of these variations are not well understood. The present study underscores the already observed phenomenon of surgical volume influencing surgical planning and outcomes, but it gets at why this might be so. We observe that high-volume renal surgeons have different thresholds of 'technical feasibility'. OBJECTIVE: To investigate why there continues to be wide variability in the application of partial nephrectomy (PN) for treating small renal masses despite guidelines in the US and Europe stating that a PN is a standard of care for a patient with a T1 renal mass. PATIENTS AND METHODS: In June 2009, 764 surgeon-members of the American Urologic Association (AUA) participated in a survey evaluating the management of renal masses. Renal mass complexity was graded by nephrometry score (NS). Multivariable logistic regression models with generalized estimating equations were constructed to evaluate how tumour, surgeon and practice-setting characteristics influence the use of PN. RESULTS: The survey response rate was 19%. Each urological surgeon responded to eight scenarios, providing 6112 evaluable cases. Tumour NS ranged from 4 to 10, and each unit increase in NS was associated with 59% increased likelihood of a surgeon offering RN on multivariable analysis (odds ratio [OR] = 1.59; 95% CI: 1.52-1.64). When holding patient and tumour characteristics constant, the following surgeon and practice-setting characteristics significantly increased the odds of offering a PN: increasing renal case volume (OR = 1.57; 95% CI: 1.27-1.95), academic practice (OR = 1.80; 95% CI: 1.42-2.29), increasing PN % volume (OR = 3.7; 95% CI: 2.46-5.55) and younger surgeon age (≤ 40 vs >50 years) (OR = 1.64; 95% CI: 1.35-1.96). CONCLUSION: The characteristics of a surgeon and the setting in which he or she practices influence the utilization of PN, the adherence to professional guidelines, and the threshold of tumour complexity at which a surgeon stops offering PN.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".