Factors that Affect Outcome of Ultrasound-Guided Radiofrequency Ablation of Renal Masses
Bibliographic record
Abstract
The purpose of this study was to examine the factors influencing the efficacy and safety of the ultrasound-guided radiofrequency ablation of renal tumors. Between January 2010 and December 2018, 159 patients with renal tumors treated with ultrasound-guided percutaneous radiofrequency ablation at our institution were included in this study. Biopsies were performed for histopathological analysis prior to each ablation. Patients underwent computed tomography follow-ups at 3, 6, and 12 months and were subsequently observed on an annual basis. The primary efficacy rate (i.e., residual tumor), local tumor progression, morbidity and mortality, and possible outcome predictors (age, body mass index, gender, tumor size, tumor location, tumor characteristics, ablation temperature, and reported technical problems) were analyzed using binary logistic regression. At the first follow-up, 3 months after ablation, the primary efficacy rate was 79%. Two percent of the tumors showed local tumor progression during the whole follow-up. Tumor proximity to the collecting system and the final temperature in the ablation region were associated with the occurrence of residual tumor (OR = 2.85, p = 0.019 and OR = 4.23, p = 0.006, respectively). A similar trend was shown for tumors larger than 3 cm (p = 0.066). A short distance to the collecting system and the ablation temperature were significantly related to the occurrence of residual tumors after the radiofrequency ablation of small renal masses. The ultrasound guidance used in our study has a lower primary efficacy rate than the computed tomography guidance used in comparable studies.
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".