Bibliographic record
Abstract
Background: Partial nephrectomy (PN) is now the gold standard for the surgical treatment of small renal masses.We evaluated the effect of WIT and other factors on RDF assessed by preoperative and postoperative renal scintigraphy.Methods: Between 2003 and 2008, 182 consecutive laparoscopic PN (LPN) were performed in an academic centre.Among those, 56 had mercaptoacetyl triglycine (MAG3) lasix renal scintigraphy preoperatively and postoperatively.Results: Medians for age, preoperative estimated glomerular filtration rate and computed tomography scan tumour size were 62 years, 82 mL/min/1.73m 2 and 26 mm, respectively.Median WIT and preoperative RDF were 30 minutes and 50%, respectively.Median loss of RDF after surgery was 14%.Linear regression curves showed that loss in RDF rate was 0.2% per minute when WIT was <30 minutes and 0.7% per minute when WIT was ≥30 minutes.In multivariate analysis, length of WIT and endophytic tumour location were associated with a statistically significant loss of RDF (p < 0.05), but only in the group who experienced >30 minutes of WIT.Interpretation: Our results suggest that the factors associated with loss of RDF are not the same before and after 30 minutes of WIT and that the rate of loss in RDF increases after 30 minutes.Since, the effect of WIT is small up to 30 minutes, we believe that surgery should focus on limiting the resection of normal parenchyma and to ensure negative margins and hemostasis, rather than on premature unclamping.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".