Bibliographic record
Abstract
Introduction: Historically, the standard of care for renal cell carcinoma (RCC) has been partial nephrectomy (PN) or radical nephrectomy (RN).Image-guided percutaneous cryoablation (PCA) of renal masses has emerged as a nephronsparing, minimally invasive alternative.In this study, we aimed to assess outcomes of percutaneous renal cryoablation at a single center over 15 years.Methods: Patients who underwent PCA of renal masses from 2006-2022 were included with no exclusions.All patients had cross-sectional imaging prior to the procedure and most underwent biopsy at the time of procedure.Cryoprobe placement and ice-ball formation were monitored via computed tomography (CT) imaging during the procedure.Data were collected via electronic medical record review, as well as pre-, intra-, and post-procedure imaging review.Data included demographics, tumor characteristics, pathology, local recurrence and metastatic disease rates, complications, pre-and post-procedure renal function and blood counts, comorbidities, and mortality rate of patients in followup.Results: This study included 598 patients in the analysis with a median age of 65.1 years and median followup of 39 months (Table 1).The average size of tumor was 2.7 cm. Overall local recurrence rate was 4.8%, including surgically resected and hereditary RCC.The average nephrometry score was 6.1.Median time to local recurrence was 2.0 years.Five percent of patients made up 52% of the recurrences.Overall survival rate was 82.3% at time of analysis.Cancer-specific survival overall was 97.3%.Average length of time to discharge was 28.2 hours.The Clavien-Dindo 3+ complication rate was 2.3%.Overall metastatic rate was 1.8% and 0.7% in patients with no recurrence.Conclusions: PCA outcomes in this large cohort with no exclusions and long-term followup revealed an overall low recurrence rate, low complication rate, and an acceptable metastatic rate.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.407 | 0.175 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".