Bibliographic record
Abstract
PURPOSE OF REVIEW: Laparoscopic partial nephrectomy (LPN) technique has continually evolved over the last decade, resulting in better outcomes and increased popularity within the urological community. In this article, we provide an overview of the contemporary literature on LPN. RECENT FINDINGS: The technique of LPN has evolved over the last 5 years with a nearly 50% reduction of warm ischemia time in experienced hands. Complication rates have also declined such that morbidity and oncological outcomes are comparable to open partial nephrectomy, the gold standard. LPN is now an established procedure for the treatment of T1a renal tumors. It can also be safely performed for favorably located T1b tumors and more complex tumors, including hilar tumors, central tumors or tumors in solitary kidneys with good oncological and functional outcomes. SUMMARY: For renal tumors less than 4-7 cm (T1 lesions), partial nephrectomy is the treatment of choice. Contemporary LPN is a sophisticated procedure, and in expert hands, offers perioperative, functional and oncologic outcomes comparable to open partial nephrectomy, even for complex tumors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".