Is laparoscopic partial nephrectomy already the gold standard for small renal masses?.
Bibliographic record
Abstract
OBJECTIVES: To examine the role of laparoscopic partial nephrectomy in the management of small renal masses. METHODS: We searched MEDLINE (through March 2012) using PubMed, the Cochrane Central Search Library (though March 2012), and Web of Science (through March 2012). We retrieved citations using the text terms "small renal mass," "laparoscopic," "partial nephrectomy,"and "radical nephrectomy." We limited the search to articles in the English language, to T1a renal tumors, and expanded the search using the related articles function. We also performed hand searches of references identified in electronically abstracted articles. RESULTS: There is a paucity of well conducted clinical trials to elucidate laparoscopic partial nephrectomy's role. A number of assumptions had to be made to complete the review. Other than possibly less operative blood loss, less operative time, less inpatient stay time, and less cost, there was insufficient evidence to support laparoscopic partial nephrectomy over other modalities. Laparoscopic partial nephrectomy appears to have a higher rate of radical nephrectomy conversion. CONCLUSION: There is insufficient evidence to clearly state that laparoscopic partial nephrectomy is the gold standard in the management of small renal masses. If this skill is part of a surgeon's armamentarium, it is certainly not inferior to other modalities, and may offer some benefit to patients.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".