Complications and blood loss after invasive treatments for small renal masses
Bibliographic record
Abstract
INTRODUCTION: This systematic review and meta-analysis provides estimates of major complications and estimated blood loss (EBL) for open partial nephrectomy (OPN), conventional laparoscopic partial nephrectomy (LPN), and robot-assisted partial nephrectomy (RAPN). Additionally, it outlines the incidence of major complications associated with percutaneous thermal ablation (TA) in patients with small renal masses (SRMs). METHODS: We searched MEDLINE, EMBASE, and CINAHL from inception to the end of July 2023. We supplemented the electronic search with a hand search of the references in the included studies and suggestions from two content experts. We used random effect meta-analysis to obtain pooled estimates of major complications and EBL. We used the QUIPS tool for risk of bias assessment and applied a prognosis approach to rate the quality of evidence using the Grades of Recommendation, Assessment, Development, and Evaluation (GRADE) framework. RESULTS: We included 65 eligible studies that provided pooled estimates of major complications after OPN of 5.4% (95% confidence interval [CI] 2.9-9.9); after conventional LPN of 4.7% (95% CI 2.6-8.3); after RAPN of 2.9% (95% CI 2.2-3.7); and after TA of 2.5% (95% CI 1.7-3.6). Pooled estimates demonstrating mean EBL of 262 ml (95% CI 200-324) for OPN; 224 ml (95% CI 193-254) for conventional LPN; and 163 ml (95% CI 136-190) for RAPN. CONCLUSIONS: This review provides the best available estimates of major complications and mean EBL after partial nephrectomy in patients with SRMs.
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 | 0.022 | 0.069 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.032 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".