Laparoscopic Partial Nephrectomy – Current State of the Art
Bibliographic record
Abstract
Clayman et al described the first successful laparoscopic nephrectomy in 1991 [1]. Since that time, laparoscopic radical nephrectomy has become the standard of care for renal tumors. At the same time, the widespread use of contemporary imaging techniques has resulted in an increased detection of small incidental renal tumors. In efforts to avoid chronic kidney disease, the management of the small renal mass has trended away from radical nephrectomy toward nephron-conserving surgery. In 1993, successful laparoscopic partial nephrectomy (LPN) was first reported in a porcine model [2]. Winfield et al reported the first human LPN in 1993 [3]. From that time, centres around the world have developed laparoscopic techniques for partial nephrectomy through retroperitoneal and transperitoneal approaches. Classically, only small, peripheral, exophytic tumors were eligible for LPN, but larger, infiltrating tumors have been managed with LPN in more recent series [4].
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".