The Learning Curve for Radical Nephrectomy for Kidney Cancer: Implications for Surgical Training
Bibliographic record
Abstract
Although radical nephrectomy (RN) is the most common treatment for kidney cancer, no data on the learning curve for RN are available. In this study we investigated the effect of surgical experience (EXP) on RN outcomes using data for 1184 patients treated with RN for a cT1–3a cN0 cM0 renal mass. EXP was defined as the total number of RNs performed by each surgeon before the patient’s operation. The primary study outcomes were all-cause mortality, clinical progression, Clavien-Dindo grade ≥2 postoperative complications (CD ≥2), and the estimated glomerular filtration rate (eGFR). Secondary outcomes were operative time, estimated blood loss, and length of stay. Multivariable analyses adjusted for case mix revealed no evidence of association between EXP and all-cause mortality (p = 0.7), clinical progression (p = 0.2), CD ≥2 (p = 0.6), or 12-mo eGFR (p = 0.9). Conversely, EXP was associated with shorter operative time (estimate −0.9; p < 0.01). Mortality, cancer control, morbidity, and renal function might not be affected by EXP. The very large cohort examined and the extensive follow-up support the validity of these negative findings. For patients with kidney cancer undergoing surgical removal of a kidney, those treated by novice surgeons have similar clinical outcomes to those treated by experienced surgeons. Thus, this procedure represents a convenient scenario for surgical training if longer operating theatre time can be planned.
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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 | 0.006 | 0.053 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".