Ablative or Surgical Treatment for Small Renal Masses (T1a): A Single-Center Comparison of Perioperative Morbidity and Complications
Bibliographic record
Abstract
The purpose of this study is to evaluate the treatment safety of thermal ablation compared to surgical treatment of T1a tumors (small renal masses) at a high-volume center. We conducted an observational single-center study based on data collected form the National Swedish Kidney Cancer Register (NSKCR) between 2015 and 2021. In total, 444 treatments of T1a tumors were included. Patients underwent surgery (partial or total nephrectomy) or ablative treatment—radiofrequency ablation (RFA) or microwave ablation (MWA). Patient characteristics were retrieved from patient records, and tumor complexity was estimated from pre-interventional CT scans. The odds ratio (OR) of suffering from a severe surgical complication following ablative treatment was estimated using a logistic regression model adjusted for age, BMI, ASA physical status classification, smoking status and RENAL nephrometry score. The frequency of severe surgical complications was 6.3% (16/256 treatments) after surgical intervention and 2.1% (4/188 treatments) following ablative treatment. Our primary hypothesis that ablative treatment is associated with a lower risk of severe surgical complications is supported by the results (OR 0.39; 0.19–0.79; p = 0.013). When adjusting for age, smoking status, ASA score, BMI score and RENAL nephrometry score, we see an even greater difference between the two groups (OR 0.34; 0.17–0.68; p = 0.002). Our study was limited by the differences in patient and tumor characteristics between the two compared groups and the study design. If oncological outcomes are found to be comparable, ablative treatment should be considered as a first-line treatment for all small renal masses.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".